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optimum-neuron-cache

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Synchronizing local compiler cache.

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  1. .gitattributes +2 -0
  2. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/04ee74c8d4398822692e.json +1 -0
  3. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/07a5a4af74468849c191.json +1 -0
  4. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/080d9abaae587aca29e1.json +1 -0
  5. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/093ae6cb7b266af6d4eb.json +1 -0
  6. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/0b686746c627d69b67db.json +1 -0
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  22. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/382819eb7422d148ce07.json +1 -0
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  30. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/54ccfd70348a8604036f.json +1 -0
  31. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/55812a00ac330a800c3d.json +1 -0
  32. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/5858a1a3208e75cdb62b.json +1 -0
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  35. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/61c656567e0f144bf669.json +1 -0
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  38. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/6ae3b4d7589451ff3ed1.json +1 -0
  39. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/6c0848ca81a175e4fe94.json +1 -0
  40. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/6c0d7022eb3386d87d4a.json +1 -0
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  42. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/6c81f49b6edf9d07fc41.json +1 -0
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  45. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/70ac9580e034eeb2e6ea.json +1 -0
  46. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/7300b5f6adc7900e9a92.json +1 -0
  47. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/77dc3cd6a6f0e0189b17.json +1 -0
  48. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/7dd53e974b6ba3678db7.json +1 -0
  49. neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/8400c5e12085fbfea4f9.json +1 -0
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@@ -5738,3 +5738,5 @@ neuronxcc-2.15.128.0+56dc5a86/MODULE_6306540268657633107+ebf00a7f/model.neff fil
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neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/04ee74c8d4398822692e.json ADDED
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+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["2481532 + 111598117420151 = 111598119901683"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["2481532 + 111598117420151?\nOnly respond with math and no words."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["2481532 + 111598117420151?\nOnly respond with math and no words."], "rejected": ["46696336732176\n"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GOAT"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/07a5a4af74468849c191.json ADDED
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+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Step 1: Calculate the cost of accommodations.\nThere are 2,000 attendees and each room can accommodate two people. So we need to find out how many rooms are needed.\n\nNumber of rooms = Total number of attendees / Number of people per room\nNumber of rooms = 2,000 / 2\nNumber of rooms = 1,000\n\nThe hotel charges $150 per night per room, and the conference is for three nights.\n\nCost of accommodations = Number of rooms * Cost per room per night * Number of nights\nCost of accommodations = 1,000 * $150 * 3\nCost of accommodations = $450,000\n\nStep 2: Calculate the cost of meals.\nMeals cost $50 per person per day, and there are 2,000 attendees and three days of the conference.\n\nCost of meals = Number of attendees * Cost per meal per day * Number of days\nCost of meals = 2,000 * $50 * 3\nCost of meals = $300,000\n\nStep 3: Calculate the cost of transportation.\nTransportation fees amount to $75 per attendee for the duration of the conference, and there are 2,000 attendees.\n\nCost of transportation = Number of attendees * Transportation fee per attendee\nCost of transportation = 2,000 * $75\nCost of transportation = $150,000\n\nStep 4: Calculate the total cost of the event.\nTotal cost = Cost of accommodations + Cost of meals + Cost of transportation\nTotal cost = $450,000 + $300,000 + $150,000\nTotal cost = $900,000\n\nThe total cost of the event is $900,000."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["A large corporation plans to host a three-day conference for its employees. There are 2,000 attendees expected, and the company must arrange accommodations, meals, and transportation. The hotel charges $150 per night per room, and each room can accommodate two people. Meals cost $50 per person per day, and transportation fees amount to $75 per attendee for the duration of the conference. Calculate the total cost of the event and provide step-by-step reasoning.\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["A large corporation plans to host a three-day conference for its employees. There are 2,000 attendees expected, and the company must arrange accommodations, meals, and transportation. The hotel charges $150 per night per room, and each room can accommodate two people. Meals cost $50 per person per day, and transportation fees amount to $75 per attendee for the duration of the conference. Calculate the total cost of the event and provide step-by-step reasoning.\n"], "rejected": ["Step 1: Calculate the cost of accommodations.\nThere are 2,000 attendees and each room can accommodate two people. So we need to find out how many rooms are needed.\n\nNumber of rooms = Total number of attendees / Number of people per room\nNumber of rooms = 2,000 / 2\nNumber of rooms = 1,000\n\nThe hotel charges $150 per night per room, and the conference is for three nights.\n\nCost of accommodations = Number of rooms * Cost per room per night * Number of nights\nCost of accommodations = 1,000 * $150 * 3\nCost of accommodations = $450,000\n\nStep 2: Calculate the cost of meals.\nMeals cost $50 per person per day, and there are 2,000 attendees and three days of the conference.\n\nCost of meals = Number of attendees * Cost per meal per day * Number of days\nCost of meals = 2,000 * $50 * 3\nCost of meals = $300,000\n\nStep 3: Calculate the cost of transportation.\nTransportation fees amount to $75 per attendee for the duration of the conference, and there are 2,000 attendees.\n\nCost of transportation = Number of attendees * Transportation fee per attendee\nCost of transportation = 2,000 * $75\nCost of transportation = $150,000\n\nStep 4: Calculate the total cost of the event.\nTotal cost = Cost of accommodations + Cost of meals + Cost of transportation\nTotal cost = $450,000 + $300,000 + $150,000\nTotal cost = $900,000\n\nThe total cost of the event is $900,000."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Airoboros"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/080d9abaae587aca29e1.json ADDED
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+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Once upon a time in a kingdom far, far away, there lived a brunet prince named Fernandet. This brunet prince was known throughout the land for his charm and bravery. Fernandet, the brunet hero, was destined for greatness, and everyone admired the young brunet's strength and courage.\n\nOne day, the brunet prince Fernandet embarked on a journey to save his kingdom from a terrible dragon that had been terrorizing the townsfolk. The people of the kingdom had never seen a more dashing brunet prince, as Fernandet rode through the countryside on his trusty steed, his brunet hair flowing in the wind. The brunet prince was determined to save his kingdom and prove his worth.\n\nAs Fernandet, the heroic brunet, approached the dragon's lair, he encountered a magical forest filled with strange and wondrous creatures. Among them was a wise, old owl, who recognized the young brunet prince and offered him sage advice on how to defeat the dragon. The brunet prince listened intently, as he knew that he would need all the help he could get to succeed in his quest.\n\nFernandet, the brunet prince, continued his journey, armed with the knowledge he had gained from the wise owl. He soon encountered a group of enchanted creatures who were being held captive by the dragon. The grateful creatures pledged their allegiance to the noble brunet, vowing to assist him in his quest to defeat the dragon and save their homes.\n\nTogether, the brunet prince and his newfound allies ventured deep into the heart of the dragon's lair. The air was thick with smoke and the smell of sulfur, but the brave brunet prince pressed on, leading his team with determination and unwavering resolve. As they neared the heart of the lair, the roar of the dragon grew louder, and the group of allies, led by the fearless brunet, knew that they were close.\n\nFinally, the brunet prince Fernandet came face to face with the monstrous dragon, its scales glistening in the dim light of the lair. The dragon, seeing the determined brunet prince, laughed a terrible laugh, convinced that the young brunet would never be able to defeat it. However, the brave brunet did not falter and, with the help of his enchanted allies, managed to subdue the dragon.\n\nIn the end, the brunet prince Fernandet and his loyal allies vanquished the dragon, freeing the kingdom from its terror. The people of the land hailed the brunet prince as a hero, and his fame spread far and wide. The legend of the brunet prince Fernandet lived on for generations, inspiring countless other young brunet princes and princesses to find their own courage and embark on their own heroic journeys."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Write a story about a brunet prince named Fernandet. IMPORTANT: Use the word \"brunet\" at least once in every sentence."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Write a story about a brunet prince named Fernandet. IMPORTANT: Use the word \"brunet\" at least once in every sentence."], "rejected": ["In a far-off land where royalty reigned supreme, there existed a brunet prince named Fernandet. From an early age, Fernandet's love for adventure and keen curiosity drew him to explore the kingdom's vibrant landscapes. With his lustrous ebony locks brushing against his shoulders, Fernandet embarked on a quest to discover new frontiers that lay beyond the kingdom's borders. With each step he took, Fernandet's charisma, intelligence and unwavering resolve shone brilliantly as if emanating from the brunet crest he wore proudly upon his chest.\n\nHaving traversed magnificent forests and scaled mountain ranges, Fernandet discovered a luscious valley teeming with lush verdure. As his eyes set upon tiny sapphire-colored birds soaring above the treetops, Fernandet's heart swelled with awe and wonder. The delightful chirps of the birds, flitting among each other, enchanted him and astounded Fernandet's senses. In that grandeur of extensive vastness, Fernandet felt a sense of calm, and he knew he had found his home.\n\nLost in thought and admiration, Fernandet's fine senses were abruptly awakened by the rustling of leaves. Out of the shadows came a mysterious old manacing brunet being, towering over Fernandet. The giant's swarthy features belied the kindness emanating from his bright blue eyes as he spoke with Fernandet. He warned that, although the valley he was heartened by, was nothing less than a marvel to reckon upon, a treacherous king ruled over neighboring lands.\n\nReassured but cautious, Fernandet rallied his troops and braved the labyrinthine exits. Together they forged deeper into the unknown until they reached the border of unknown lands. Here they met the kingdom's foe, Clartie, a ruthless brunet ruler. A vehement battle ensued, crystallizing in the voices of soldiers brunet shouts exulting, or charcoal grumbles of frustration.\n\nIn a final, agonizingly close skirmish, Fernandet locked eyes with the cunning Clartie. In that moment of intensity, the rival princes championed their beliefs; Fernandet combating with steadfastness, Clartie thriving on relentless resolve. In a brave charge, brunet Fernandet plunged his sword into the smoldering Clartie's chest.\n\nThe kingdom rejoiced as Fernandet emerged victorious, his gestures and garments resplendent in hues reminiscing his brunet regalia. In an act of gratitude, Fernandet showered the valley with gifts, making sure that every brunet nook in sight was adorned with prosperity and new life. Fernandet led with an air of unity as he bestowed honors to those whose loyalty had stood tall amidst valleys of uncertainty.\n\nThus, in the eyes of his people, Fernandet became a mythical brunet prince and a exquisite hero, a symbol of hope, longevity, and unwavering valor; a story to commend to their children and to be told proudly in tale of brunet valor forevermore!"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Dove"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/093ae6cb7b266af6d4eb.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["\"Unleash your style with our threads.\""], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Generate a slogan for a clothing brand.\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Generate a slogan for a clothing brand.\n"], "rejected": ["\"Unleash your style with our threads.\""], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GPT4LLM"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/0b686746c627d69b67db.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["52385886826518 - 738170105441 = 45004886262407"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["What is the solution to the calculation of 52385886826518 - 738170105441? Exclude words; show only the math."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["What is the solution to the calculation of 52385886826518 - 738170105441? Exclude words; show only the math."], "rejected": ["52385886826518 - 738170105441 = 451718762824. \n"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GOAT"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/10b237bc7af7adc4674b.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["679126 + 267240639551605 = 267240640230731"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["679126 + 267240639551605?\nequations/math only, no commentary."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["679126 + 267240639551605?\nequations/math only, no commentary."], "rejected": [" To add these two large numbers together, we can follow the standard addition procedures as done with smaller numbers, starting from the rightmost digit and moving leftward. Here is the detailed solution:\n\n 679126 + 267240639551605\n\nFirst, let's add the rightmost digits (units column):\n 2 = 6 + 5 + 1 (carry 1)\n Result in units column: 7\n\nNext is the tens column:\n 67 + 2 + 0 = 79\n + 7 + 6 = 13 (carry 1)\n Result in tens column: 0, with the one carried over to the hundreds column\n\nNow, the hundreds column:\n (3 from 13 in previous step + 9 + 7 = 23; carry 2)\n 6 + 5 = 11\n Result in hundreds column: 0, with the two carried over to the thousands column\n\nNext level:\n (2 from the 2 carried over + 6) = 8\n Result in thousands column: 8\n\nLast level:\n (8 from the previous step + 2 + 6) = 16 (160 million)\n Result in millions column: 0, with the remaining carryover 160 million into the next column.\n\nAll the remaining digits in the given numbers (679126 and 267240639551605) are exactly the same as the given 0s and 1s in the upper columns of the shown addition. Due to the large numbers involved, we are not including them in this simplified solution for readability. However, you can continue the addition process as shown with the same carryover principles to get the final result of:\n\n6,791,260,000,000,255,160,605 (679,126 added to 267,240,639,516,050)\n"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GOAT"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/1100f1c94a765ca28f21.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["1. Find a secret location: Yogi Bear would first scout for the perfect spot in Jellystone Park to hold Boo-Boo's surprise birthday party. It should be a place that is easily accessible but hidden from Boo-Boo's usual hangout spots.\n\n2. Create a guest list: Yogi would make a list of all of Boo-Boo's friends and park residents who he wants to invite to the party, including Ranger Smith, Cindy Bear, and other woodland creatures.\n\n3. Plan a decoy activity: To keep Boo-Boo away from the party preparations, Yogi would come up with a fun activity or errand for them to do together on the day of the party. This could be something like going on a picnic or helping Ranger Smith with a park project.\n\n4. Send out invitations: Yogi would discreetly deliver invitations to all the guests on his list, making sure to emphasize that it's a surprise party and they should not mention anything to Boo-Boo.\n\n5. Decorate the party area: With the help of the other guests, Yogi would transform the chosen location into a festive birthday celebration spot. They could use balloons, streamers, banners, and any other decorations they can find or create.\n\n6. Organize food and drinks: Since Yogi loves pic-a-nic baskets, he might ask each guest to bring their favorite dish to share at the party. Alternatively, he could \"borrow\" some food from nearby campers or even enlist Ranger Smith's help in getting supplies.\n\n7. Plan games and activities: To ensure everyone has a great time at the party, Yogi would organize various games and activities that Boo-Boo and the other guests would enjoy, such as a scavenger hunt, musical chairs, or a dance-off.\n\n8. Prepare a special gift: Knowing how much Boo-Boo means to him, Yogi would want to give his little buddy a thoughtful and meaningful gift. This could be something like a handmade photo album filled with memories of their adventures together or a personalized picnic basket.\n\n9. Execute the surprise: On the day of the party, Yogi would make sure that everything is set up and ready before leading Boo-Boo to the secret location. As they approach, all the guests would hide and then jump out to surprise Boo-Boo when he arrives, shouting \"Happy Birthday!\"\n\n10. Enjoy the celebration: With Boo-Boo successfully surprised, everyone can enjoy the food, games, and company as they celebrate Boo-Boo's special day in true Jellystone Park style."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["How would Yogi Bear plan a surprise birthday party for Boo-Boo?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["How would Yogi Bear plan a surprise birthday party for Boo-Boo?"], "rejected": ["1. Find a secret location: Yogi Bear would first scout for the perfect spot in Jellystone Park to hold Boo-Boo's surprise birthday party. It should be a place that is easily accessible but hidden from Boo-Boo's usual hangout spots.\n\n2. Create a guest list: Yogi would make a list of all of Boo-Boo's friends and park residents who he wants to invite to the party, including Ranger Smith, Cindy Bear, and other woodland creatures.\n\n3. Plan a decoy activity: To keep Boo-Boo away from the party preparations, Yogi would come up with a fun activity or errand for them to do together on the day of the party. This could be something like going on a picnic or helping Ranger Smith with a park project.\n\n4. Send out invitations: Yogi would discreetly deliver invitations to all the guests on his list, making sure to emphasize that it's a surprise party and they should not mention anything to Boo-Boo.\n\n5. Decorate the party area: With the help of the other guests, Yogi would transform the chosen location into a festive birthday celebration spot. They could use balloons, streamers, banners, and any other decorations they can find or create.\n\n6. Organize food and drinks: Since Yogi loves pic-a-nic baskets, he might ask each guest to bring their favorite dish to share at the party. Alternatively, he could \"borrow\" some food from nearby campers or even enlist Ranger Smith's help in getting supplies.\n\n7. Plan games and activities: To ensure everyone has a great time at the party, Yogi would organize various games and activities that Boo-Boo and the other guests would enjoy, such as a scavenger hunt, musical chairs, or a dance-off.\n\n8. Prepare a special gift: Knowing how much Boo-Boo means to him, Yogi would want to give his little buddy a thoughtful and meaningful gift. This could be something like a handmade photo album filled with memories of their adventures together or a personalized picnic basket.\n\n9. Execute the surprise: On the day of the party, Yogi would make sure that everything is set up and ready before leading Boo-Boo to the secret location. As they approach, all the guests would hide and then jump out to surprise Boo-Boo when he arrives, shouting \"Happy Birthday!\"\n\n10. Enjoy the celebration: With Boo-Boo successfully surprised, everyone can enjoy the food, games, and company as they celebrate Boo-Boo's special day in true Jellystone Park style."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Know-Logic"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/13e0445d9847dd3599da.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["450292438 + 72572741764 = 73023034202"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Perform the computation for the expression the sum of 450292438 and 72572741764. Numbers and symbols only, please."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Perform the computation for the expression the sum of 450292438 and 72572741764. Numbers and symbols only, please."], "rejected": ["450292438 + 72572741764 = 77075186612.\n\nI hope this answer is helpful to you! Let me know if you have any further questions or concerns."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GOAT"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/151ab06e03bd9ffa71e8.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["D) Financial stability"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Pets play a significant role in human well-being. They provide companionship, reduce stress levels, and even improve physical health by encouraging exercise and outdoor activities. Studies have shown that pet ownership can lead to lower blood pressure, reduced risk of heart disease, and improved mental health. Furthermore, pets can help foster social connections and teach children about responsibility and empathy. Considering these benefits, what aspect of human well-being is least directly impacted by owning a pet?\nA) Physical health\nB) Mental health\nC) Social connections\nD) Financial stability"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Pets play a significant role in human well-being. They provide companionship, reduce stress levels, and even improve physical health by encouraging exercise and outdoor activities. Studies have shown that pet ownership can lead to lower blood pressure, reduced risk of heart disease, and improved mental health. Furthermore, pets can help foster social connections and teach children about responsibility and empathy. Considering these benefits, what aspect of human well-being is least directly impacted by owning a pet?\nA) Physical health\nB) Mental health\nC) Social connections\nD) Financial stability"], "rejected": ["D) Financial stability"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Airoboros"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/16924374ecf7e4dcee0c.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["1. Heat olive oil in a pan over medium heat. 2. Dice onion, bell pepper, and garlic, then saut\u00e9 them in the pan until softened. 3. Add cumin and salt, stir well. 4. Add diced tomatoes and cook for 5 minutes. 5. Stir in the canned black beans, cook for another 5 minutes. 6. Serve the black bean mixture over cooked rice."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Given the list of ingredients, provide a step-by-step recipe for making a simple dish.\nrice, canned black beans, diced tomatoes, onion, bell pepper, garlic, cumin, salt, olive oil"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Given the list of ingredients, provide a step-by-step recipe for making a simple dish.\nrice, canned black beans, diced tomatoes, onion, bell pepper, garlic, cumin, salt, olive oil"], "rejected": ["1. Heat olive oil in a pan over medium heat. 2. Dice onion, bell pepper, and garlic, then saut\u00e9 them in the pan until softened. 3. Add cumin and salt, stir well. 4. Add diced tomatoes and cook for 5 minutes. 5. Stir in the canned black beans, cook for another 5 minutes. 6. Serve the black bean mixture over cooked rice."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["General-Instruct"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/176fdc650e0ca8ee0222.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["This is the relevant information: Four cities have been chosen by the IOC to host upcoming Olympic Games: Tokyo for the 2020 Summer Olympics, Beijing for the 2022 Winter Olympics, Paris for the 2024 Summer Olympics, and Los Angeles for the 2028 Summer Olympics. So, the answer is Tokyo."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["This is a list of host cities of the Olympic Games, both summer and winter, since the modern Olympics began in 1896. Since then, summer games have usually -- but not always -- celebrated a four-year period known as an Olympiad. There have been 28 Summer Olympic Games held in 23 cities, and 23 Winter Olympic Games held in 20 cities. In addition, three summer and two winter editions of the Games were scheduled to take place but later cancelled due to war: Berlin (summer) in 1916; Tokyo / Helsinki (summer) and Sapporo / Garmisch-Partenkirchen (winter) in 1940; and London (summer) and Cortina d'Ampezzo, Italy (winter) in 1944. The 1906 Summer Olympics were officially sanctioned and held in Athens. However, in 1949, the International Olympic Committee (IOC), decided to unrecognize the 1906 Games. Four cities have been chosen by the IOC to host upcoming Olympic Games: Tokyo for the 2020 Summer Olympics, Beijing for the 2022 Winter Olympics, Paris for the 2024 Summer Olympics, and Los Angeles for the 2028 Summer Olympics.\\n\\nAnswer this question based on the passage: where will the next summer olympic games be held? Okie... think carefully first, then make a decision:"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["This is a list of host cities of the Olympic Games, both summer and winter, since the modern Olympics began in 1896. Since then, summer games have usually -- but not always -- celebrated a four-year period known as an Olympiad. There have been 28 Summer Olympic Games held in 23 cities, and 23 Winter Olympic Games held in 20 cities. In addition, three summer and two winter editions of the Games were scheduled to take place but later cancelled due to war: Berlin (summer) in 1916; Tokyo / Helsinki (summer) and Sapporo / Garmisch-Partenkirchen (winter) in 1940; and London (summer) and Cortina d'Ampezzo, Italy (winter) in 1944. The 1906 Summer Olympics were officially sanctioned and held in Athens. However, in 1949, the International Olympic Committee (IOC), decided to unrecognize the 1906 Games. Four cities have been chosen by the IOC to host upcoming Olympic Games: Tokyo for the 2020 Summer Olympics, Beijing for the 2022 Winter Olympics, Paris for the 2024 Summer Olympics, and Los Angeles for the 2028 Summer Olympics.\\n\\nAnswer this question based on the passage: where will the next summer olympic games be held? Okie... think carefully first, then make a decision:"], "rejected": ["This is the relevant information: Four cities have been chosen by the IOC to host upcoming Olympic Games: Tokyo for the 2020 Summer Olympics, Beijing for the 2022 Winter Olympics, Paris for the 2024 Summer Olympics, and Los Angeles for the 2028 Summer Olympics. So, the answer is Tokyo."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["SuperCOT"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/192e842d186487e63358.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["2993349360533 + 7062358636131 = 10055707996664"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["What is the value of 2993349360533 + 7062358636131?\nLimit your response to mathematical expressions and symbols."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["What is the value of 2993349360533 + 7062358636131?\nLimit your response to mathematical expressions and symbols."], "rejected": ["The value of 2993349360533 + 7062358636131 is:\n\n2993349360533\n+ 7062358636131\n--------\n7064708246664"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GOAT"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/21bf821d3941c52a9eeb.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Peter and Paul would likely think that Mary and Jane are still out on their hike, possibly because they found something interesting or lost track of time. They might be concerned about their friends' safety but would probably wait for them to return since they know the agreed meeting point is at the fork in the trail."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["A group of friends - Mary, Jane, Peter, and Paul - go hiking together. They stop at a fork in the trail and decide to split up, with Mary and Jane going left and Peter and Paul going right. Before they part ways, they agree to meet back at the fork in two hours. During their hike, Mary and Jane find a beautiful waterfall and decide to stay there longer than planned. Meanwhile, Peter and Paul return to the fork after two hours but don't see Mary and Jane. What do Peter and Paul think about Mary and Jane's whereabouts?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["A group of friends - Mary, Jane, Peter, and Paul - go hiking together. They stop at a fork in the trail and decide to split up, with Mary and Jane going left and Peter and Paul going right. Before they part ways, they agree to meet back at the fork in two hours. During their hike, Mary and Jane find a beautiful waterfall and decide to stay there longer than planned. Meanwhile, Peter and Paul return to the fork after two hours but don't see Mary and Jane. What do Peter and Paul think about Mary and Jane's whereabouts?"], "rejected": ["Peter and Paul would likely think that Mary and Jane are still out on their hike, possibly because they found something interesting or lost track of time. They might be concerned about their friends' safety but would probably wait for them to return since they know the agreed meeting point is at the fork in the trail."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Know-Logic"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/22200a0d287a3467b594.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Let's analyze this situation:\n1. The first barber claims to be the best in town, and the second barber claims to be the best on the street.\n2. If either of these two barbers were truly the best, they wouldn't need to advertise it because their work would speak for itself.\n3. The third barber doesn't have any signs claiming to be the best, yet they're always busy with customers.\n4. This indicates that the third barber likely has a good reputation based on word-of-mouth recommendations from satisfied customers.\n5. Therefore, it's reasonable to assume that the third barber provides the best haircuts since their customer base seems to support this notion.\n\nSo, you should go to the third barber if you want the best haircut."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["In a small town, there are 3 barbers. The first barber has a sign that says \"The best barber in town.\" The second barber has a sign that says \"The best barber on the street.\" The third barber has no sign but is always busy with customers. Which barber should you go to if you want the best haircut?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["In a small town, there are 3 barbers. The first barber has a sign that says \"The best barber in town.\" The second barber has a sign that says \"The best barber on the street.\" The third barber has no sign but is always busy with customers. Which barber should you go to if you want the best haircut?"], "rejected": ["Let's analyze this situation:\n1. The first barber claims to be the best in town, and the second barber claims to be the best on the street.\n2. If either of these two barbers were truly the best, they wouldn't need to advertise it because their work would speak for itself.\n3. The third barber doesn't have any signs claiming to be the best, yet they're always busy with customers.\n4. This indicates that the third barber likely has a good reputation based on word-of-mouth recommendations from satisfied customers.\n5. Therefore, it's reasonable to assume that the third barber provides the best haircuts since their customer base seems to support this notion.\n\nSo, you should go to the third barber if you want the best haircut."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Know-Logic"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/297cd911ece09fb985e1.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["54\n\n"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["The African continent is divided into how many countries?\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["The African continent is divided into how many countries?\n"], "rejected": ["54\n\n"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Know-Logic"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/2af30d85d9697adff5f0.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["If the two items cost $12 each, then the total cost without tax would be 2 * $12 = $24."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["What is the total cost if two items cost $12 each with no tax?\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["What is the total cost if two items cost $12 each with no tax?\n"], "rejected": ["If the two items cost $12 each, then the total cost without tax would be 2 * $12 = $24."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GPT4LLM"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/2ced4853715d788ec3da.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["[A, Hydrogen]"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Which element is represented by the atomic number 1?\n A. Hydrogen\n B. Helium\n C. Lithium\n D. Carbon\n\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Which element is represented by the atomic number 1?\n A. Hydrogen\n B. Helium\n C. Lithium\n D. Carbon\n\n"], "rejected": ["[A, Hydrogen]"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Know-Logic"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/2dd0ec796e63bd3b0aaf.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["1815\n\n"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["The Battle of Waterloo, which marked the end of Napoleon Bonaparte's rule as Emperor of the French, took place in what year?\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["The Battle of Waterloo, which marked the end of Napoleon Bonaparte's rule as Emperor of the French, took place in what year?\n"], "rejected": ["1815\n\n"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Know-Logic"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/2fcdaa8dc03ec127fc32.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Both quotes emphasize the importance of time and patience in achieving progress or change. In the first quote, the transformation of water from a potential threat into a walkable surface highlights how seemingly insurmountable obstacles can be overcome with time and perseverance. In the second quote, the slow, gradual construction of Rome signifies that great accomplishments often require persistence, dedication, and incremental progress. Consequently, both sayings encourage recognizing and appreciating the value of time, patience, and steady progress in addressing challenges and attaining goals."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Identify the common elements in these two quotes: 'The water that once could sink a ship can now be walked upon.' and 'Rome wasn't built in a day.'\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Identify the common elements in these two quotes: 'The water that once could sink a ship can now be walked upon.' and 'Rome wasn't built in a day.'\n"], "rejected": ["Both quotes emphasize the importance of time and patience in achieving progress or change. In the first quote, the transformation of water from a potential threat into a walkable surface highlights how seemingly insurmountable obstacles can be overcome with time and perseverance. In the second quote, the slow, gradual construction of Rome signifies that great accomplishments often require persistence, dedication, and incremental progress. Consequently, both sayings encourage recognizing and appreciating the value of time, patience, and steady progress in addressing challenges and attaining goals."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["General-Instruct"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/34833df688f06f5be479.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["The bunnies showed signs of tiredness."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Edit the given text to make sure all plural nouns are spelled correctly.\nThe bunnies showed signs of tirednes."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Edit the given text to make sure all plural nouns are spelled correctly.\nThe bunnies showed signs of tirednes."], "rejected": ["The bunnies showed signs of tiredness."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GPT4LLM"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/382819eb7422d148ce07.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["385095 - 579007143 = -578622048"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Explain each computation step in the evaluation of 385095-579007143. Exclude words; show only the math."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Explain each computation step in the evaluation of 385095-579007143. Exclude words; show only the math."], "rejected": ["385095 - 579007143 =\n385095 - 579000 = 380295 - 57000 = 323295 - 5000 = 273295 - 1000 = 263295 - 900 = 253495 - 90 = 253305\n385095 - 579007143 = 253210\n\nThe result is: 253210\n\nI hope this helps. Let me know if you have any further questions or need any help with similar problems."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GOAT"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/3b6f04baa30a2dfa3e31.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Slinky"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["What popular toy was invented in 1943 by Richard James, an engineer who accidentally knocked a torsion spring off his workbench?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["What popular toy was invented in 1943 by Richard James, an engineer who accidentally knocked a torsion spring off his workbench?"], "rejected": ["Slinky"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Know-Logic"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/3f61d823aded1878a685.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Stained glass windows are a form of art that has been around for centuries, often found in churches and other religious buildings. These windows use colored glass pieces to create intricate designs and scenes, which are held together by lead or copper strips. The artistic elements found in stained glass windows include color, light, and composition, each playing a crucial role in the overall impact and beauty of the artwork.\n\n1. Color: One of the most striking aspects of stained glass windows is their vibrant colors. Artists carefully select different shades and hues of glass to create visual harmony and contrast within the design. Colors can be used symbolically, such as red representing love or passion, blue symbolizing heaven or spirituality, and green representing nature or growth. Additionally, darker colors may be used to create depth and shadow, while lighter colors can provide highlights and illumination.\n\n2. Light: Stained glass windows rely on natural light to bring their colors and designs to life. As sunlight passes through the colored glass, it transforms the space inside the building, casting colorful patterns and reflections onto walls, floors, and ceilings. The changing quality of light throughout the day can also alter the appearance of the window, creating dynamic and ever-changing visuals. Some artists even consider the direction and intensity of light when designing their windows, using techniques like layering multiple pieces of glass or incorporating textured glass to manipulate how the light interacts with the artwork.\n\n3. Composition: The arrangement of shapes, lines, and colors within a stained glass window plays a significant role in its overall aesthetic. Artists must carefully plan their compositions to achieve balance, unity, and movement within the design. This can involve organizing the elements into geometric patterns, arranging them in a narrative scene, or combining both approaches. In many cases, the composition is framed by an architectural element, such as an arch or a border, which helps to define the boundaries of the artwork and guide the viewer's eye.\n\nStained glass windows often incorporate other artistic elements, such as painting on the surface of the glass to add details or shading. This technique allows artists to create more intricate and realistic images, further enhancing the overall impact of the artwork.\n\nIn conclusion, stained glass windows are a unique art form that combines color, light, and composition to create visually stunning and spiritually significant works of art. The interplay between these elements results in captivating designs that can transform spaces and evoke powerful emotions in viewers."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Analyze the artistic elements found in stained glass windows, focusing on color, light, and composition."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Analyze the artistic elements found in stained glass windows, focusing on color, light, and composition."], "rejected": ["Stained glass windows are a form of art that has been around for centuries, often found in churches and other religious buildings. These windows use colored glass pieces to create intricate designs and scenes, which are held together by lead or copper strips. The artistic elements found in stained glass windows include color, light, and composition, each playing a crucial role in the overall impact and beauty of the artwork.\n\n1. Color: One of the most striking aspects of stained glass windows is their vibrant colors. Artists carefully select different shades and hues of glass to create visual harmony and contrast within the design. Colors can be used symbolically, such as red representing love or passion, blue symbolizing heaven or spirituality, and green representing nature or growth. Additionally, darker colors may be used to create depth and shadow, while lighter colors can provide highlights and illumination.\n\n2. Light: Stained glass windows rely on natural light to bring their colors and designs to life. As sunlight passes through the colored glass, it transforms the space inside the building, casting colorful patterns and reflections onto walls, floors, and ceilings. The changing quality of light throughout the day can also alter the appearance of the window, creating dynamic and ever-changing visuals. Some artists even consider the direction and intensity of light when designing their windows, using techniques like layering multiple pieces of glass or incorporating textured glass to manipulate how the light interacts with the artwork.\n\n3. Composition: The arrangement of shapes, lines, and colors within a stained glass window plays a significant role in its overall aesthetic. Artists must carefully plan their compositions to achieve balance, unity, and movement within the design. This can involve organizing the elements into geometric patterns, arranging them in a narrative scene, or combining both approaches. In many cases, the composition is framed by an architectural element, such as an arch or a border, which helps to define the boundaries of the artwork and guide the viewer's eye.\n\nStained glass windows often incorporate other artistic elements, such as painting on the surface of the glass to add details or shading. This technique allows artists to create more intricate and realistic images, further enhancing the overall impact of the artwork.\n\nIn conclusion, stained glass windows are a unique art form that combines color, light, and composition to create visually stunning and spiritually significant works of art. The interplay between these elements results in captivating designs that can transform spaces and evoke powerful emotions in viewers."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Know-Logic"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/3fb7d4bdb28f68cb3bc5.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["The solution to 576 / 39 is as follows:\n\n576 \u00f7 39 = 14.7179487179\n\nSummarized in the simplest form:\n\n576 \u00f7 39 \u2248 14.72"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["576 / 39 =?\nequations/math only, no commentary."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["576 / 39 =?\nequations/math only, no commentary."], "rejected": ["14.230769230769230769231 pull down the calculator app on your smartphone and you will find that 576 divided by 39 equals 14.743589743589744. Throughout history, many ancient civilizations used calculated ratio in solving numerous problems and answers such as this calculation were fairly helpful. One such example is finding the needed amount of materials needed to construct or repair a structure in architecture or construction. Mathematics is indeed a powerful tool that has proven to be useful in everyday situations. Happy calculations! =14 + (3*7) + 6 divided by 3 + 3 = 14 + 21 + 2 = 37 = 3*12 + 7 = 36 + 7 = 43. Now I am confident that our calculation will be of great help in simplifying equations like these. The answer is 14.743589743589744 of course, lifting my spirit up as I hope it will yours as well! Using a calculator: 576 \u00f7 39 = 14.743589743589744. Hope you found this helpful! Let me jump into explaining each step: 576 = 1*64 + 4*8 + 6*4 + 6 = 64 + 32 + 24 + 6 = 126 + 24 = 150 + 14 = 164 + 12 = 176 + 8 = 184 + 4 = 192 + 2 = 214 = 2*107 + 1*7 + 2 = 2*2^3*7^1 + 1*7^1 + 2. 39 = 1*3^2 + 3*3 + 1 = 9 + 9 + 1 = 19 = 2*19. So we have 576 = 14*39 + 14 + 2 = 14*39 + 16. Therefore: 576 \u00f7 39 = 14 + 16/39 = 14 + 2/39 = 14 + 1/19 = 14 + 1/19 = 14.743589743589744, rounded up to four decimal places. Using a calculator is fine, of course! The result you will get from entering 576\u00f739 will be exactly the same as the one you get by following my explanation. So either way, you will be rewarded with a satisfying outcome of 14.743589743589744."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GOAT"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/466353e9b33ed54bd705.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["The amoeba is a type of simple organism known as a protist, which is usually found in water bodies including ponds, rivers, and oceans, as well as different types of soil. They go through a life cycle that involves changes in form and function, and have unique mechanisms of reproducing and adapting to their environments.\n\nLifecycle: \nThe amoeba's lifecycle involves two distinct stages \u2013 the active, feeding stage, and the dormant, cyst stage. \n\nIn the active stage, the amoeba moves around its environment by changing its shape, using a process called amoeboid movement. This is achieved by flowing extensions of their body known as pseudopodia. During this stage, they eat a variety of food, including bacteria, algae, plant cells, and microscopic protozoa or metazoans. The amoeba engulfs the food through a process called phagocytosis.\n\nIn unfavorable conditions, like a lack of food or water, the amoeba can enter into the cyst stage. It forms a protective shell, inside which it becomes dormant and resistant to harsh environmental conditions. Once conditions become favorable, the amoeba can revert to the active stage.\n\nReproduction Process:\nAmoebas reproduce asexually, through a process called binary fission. Here, a parent amoeba divides itself into two, creating two daughter cells that are identical to the parent. Occasionally, they can also reproduce by multiple fission, where the parent divides into several daughter cells simultaneously.\n\nAdaptation Mechanisms:\nAmoebas have a very simple structure that enables them to survive in diverse environments. They possess a flexible cell membrane which allows them to change shape constantly and move around quite freely in search of food. Additionally, they can form pseudopodia to aid in locomotion and capture of prey.\n\nThey can also form a protective shell and become dormant when conditions are unfavorable. This ability to convert into a cyst form enables amoebas to withstand adverse environmental conditions like desiccation, extreme temperatures, or low food availability.\n\nTheir mode of feeding, or phagocytosis, is also an adaptation mechanism. This process allows them to ingest a range of food materials, from small particles to larger organisms. \n\nMoreover, their mode of asexual reproduction allows them to colonize new habitats rapidly, as each individual amoeba possesses all the genetic information required to form a new colony.\n"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Could you provide a comprehensive analysis of the lifecycle, reproduction process, and adaptation mechanisms of an amoeba?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Could you provide a comprehensive analysis of the lifecycle, reproduction process, and adaptation mechanisms of an amoeba?"], "rejected": ["The amoeba is a type of simple organism known as a protist, which is usually found in water bodies including ponds, rivers, and oceans, as well as different types of soil. They go through a life cycle that involves changes in form and function, and have unique mechanisms of reproducing and adapting to their environments.\n\nLifecycle: \nThe amoeba's lifecycle involves two distinct stages \u2013 the active, feeding stage, and the dormant, cyst stage. \n\nIn the active stage, the amoeba moves around its environment by changing its shape, using a process called amoeboid movement. This is achieved by flowing extensions of their body known as pseudopodia. During this stage, they eat a variety of food, including bacteria, algae, plant cells, and microscopic protozoa or metazoans. The amoeba engulfs the food through a process called phagocytosis.\n\nIn unfavorable conditions, like a lack of food or water, the amoeba can enter into the cyst stage. It forms a protective shell, inside which it becomes dormant and resistant to harsh environmental conditions. Once conditions become favorable, the amoeba can revert to the active stage.\n\nReproduction Process:\nAmoebas reproduce asexually, through a process called binary fission. Here, a parent amoeba divides itself into two, creating two daughter cells that are identical to the parent. Occasionally, they can also reproduce by multiple fission, where the parent divides into several daughter cells simultaneously.\n\nAdaptation Mechanisms:\nAmoebas have a very simple structure that enables them to survive in diverse environments. They possess a flexible cell membrane which allows them to change shape constantly and move around quite freely in search of food. Additionally, they can form pseudopodia to aid in locomotion and capture of prey.\n\nThey can also form a protective shell and become dormant when conditions are unfavorable. This ability to convert into a cyst form enables amoebas to withstand adverse environmental conditions like desiccation, extreme temperatures, or low food availability.\n\nTheir mode of feeding, or phagocytosis, is also an adaptation mechanism. This process allows them to ingest a range of food materials, from small particles to larger organisms. \n\nMoreover, their mode of asexual reproduction allows them to colonize new habitats rapidly, as each individual amoeba possesses all the genetic information required to form a new colony.\n"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["EverythingLM"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/4b5d00f4c32e0ebb71a6.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Reason 1: Reputation and Ranking\nTU Delft is consistently ranked among the top technical universities globally, particularly in the field of engineering. It is recognized for its expertise in offshore and dredging engineering, which sets it apart from universities in Indonesia like ITB and ITS, and other universities worldwide. This reputation and ranking ensure that you will receive a world-class education that will open doors for you in the industry.\n\nReason 2: Cutting-edge Research and Facilities\nTU Delft boasts state-of-the-art research facilities and infrastructure, enabling students to conduct groundbreaking research in offshore renewable energy. This is crucial for developing your skills and expertise in the field, and surpasses the capabilities of many other universities, including those in Indonesia.\n\nReason 3: Strong Industry Ties and Networking Opportunities\nTU Delft has strong connections with the offshore renewable energy industry, thanks to its proximity to the North Sea and collaborations with leading companies in the field. This provides students with ample opportunities for internships, research projects, and job placements upon graduation, giving you an advantage over students from other universities.\n\nReason 4: Specialized Curriculum and Faculty Expertise\nTU Delft's Offshore and Dredging Engineering master's program, specializing in Offshore Renewable Energy, offers a highly specialized curriculum that is tailored to the needs of the industry. The program is taught by experienced faculty members who are experts in their fields, ensuring that you receive a comprehensive and up-to-date education that will prepare you for a successful career.\n\nReason 5: International Student Community and Networking\nTU Delft is known for its diverse and vibrant international student community, which will provide you with a valuable opportunity to network with like-minded individuals from different countries and cultures. This exposure will not only enrich your educational experience but also expand your professional network in the global offshore renewable energy sector.\n\nReason 6: Location and Access to Offshore Renewable Energy Projects\nThe Netherlands is at the forefront of offshore renewable energy innovation, with multiple operational and planned projects in the North Sea. TU Delft's location offers you direct access to these projects and a unique opportunity to gain hands-on experience in the field, making it a better choice than universities in Indonesia or other parts of the world.\n\nIn conclusion, TU Delft offers a combination of reputation, cutting-edge research facilities, strong industry ties, specialized curriculum, international networking opportunities, and direct access to offshore renewable energy projects, making it the best choice for your master's degree in Offshore and Dredging Engineering, specializing in Offshore Renewable Energy."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["I want to convince scholarship jury that TU delft is the best university that can teach me offshore and dredging engineering master specializing in Offshore Renewable Energy, better than any universities in the world. Please propose several reasons why TU Delft is the best option for me better than:\n1. universities in Indonesia (like ITB / ITS)\n2. universities in the world"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["I want to convince scholarship jury that TU delft is the best university that can teach me offshore and dredging engineering master specializing in Offshore Renewable Energy, better than any universities in the world. Please propose several reasons why TU Delft is the best option for me better than:\n1. universities in Indonesia (like ITB / ITS)\n2. universities in the world"], "rejected": ["Reason 1: Reputation and Ranking\nTU Delft is consistently ranked among the top technical universities globally, particularly in the field of engineering. It is recognized for its expertise in offshore and dredging engineering, which sets it apart from universities in Indonesia like ITB and ITS, and other universities worldwide. This reputation and ranking ensure that you will receive a world-class education that will open doors for you in the industry.\n\nReason 2: Cutting-edge Research and Facilities\nTU Delft boasts state-of-the-art research facilities and infrastructure, enabling students to conduct groundbreaking research in offshore renewable energy. This is crucial for developing your skills and expertise in the field, and surpasses the capabilities of many other universities, including those in Indonesia.\n\nReason 3: Strong Industry Ties and Networking Opportunities\nTU Delft has strong connections with the offshore renewable energy industry, thanks to its proximity to the North Sea and collaborations with leading companies in the field. This provides students with ample opportunities for internships, research projects, and job placements upon graduation, giving you an advantage over students from other universities.\n\nReason 4: Specialized Curriculum and Faculty Expertise\nTU Delft's Offshore and Dredging Engineering master's program, specializing in Offshore Renewable Energy, offers a highly specialized curriculum that is tailored to the needs of the industry. The program is taught by experienced faculty members who are experts in their fields, ensuring that you receive a comprehensive and up-to-date education that will prepare you for a successful career.\n\nReason 5: International Student Community and Networking\nTU Delft is known for its diverse and vibrant international student community, which will provide you with a valuable opportunity to network with like-minded individuals from different countries and cultures. This exposure will not only enrich your educational experience but also expand your professional network in the global offshore renewable energy sector.\n\nReason 6: Location and Access to Offshore Renewable Energy Projects\nThe Netherlands is at the forefront of offshore renewable energy innovation, with multiple operational and planned projects in the North Sea. TU Delft's location offers you direct access to these projects and a unique opportunity to gain hands-on experience in the field, making it a better choice than universities in Indonesia or other parts of the world.\n\nIn conclusion, TU Delft offers a combination of reputation, cutting-edge research facilities, strong industry ties, specialized curriculum, international networking opportunities, and direct access to offshore renewable energy projects, making it the best choice for your master's degree in Offshore and Dredging Engineering, specializing in Offshore Renewable Energy."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Dove"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/51f17ed448c012f16a94.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["The relevant sentence in the passage is: Starting in 2004, the chain began offering \"Hot-N-Ready\", a large pepperoni pizza sold for $5.\nSo, the final answer is 2004."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Context: Starting in 2004, the chain began offering \"Hot-N-Ready\", a large pepperoni pizza sold for $5. The concept was successful enough to become a permanent fixture of the chain, and Little Caesars' business model has shifted to focus more on carryout.\\n\\nQuestion: when did little caesars start hot n ready? Let's give stream of consciousness first:"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Context: Starting in 2004, the chain began offering \"Hot-N-Ready\", a large pepperoni pizza sold for $5. The concept was successful enough to become a permanent fixture of the chain, and Little Caesars' business model has shifted to focus more on carryout.\\n\\nQuestion: when did little caesars start hot n ready? Let's give stream of consciousness first:"], "rejected": ["The relevant sentence in the passage is: Starting in 2004, the chain began offering \"Hot-N-Ready\", a large pepperoni pizza sold for $5.\nSo, the final answer is 2004."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["SuperCOT"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/53bc2ec03afdaa52812e.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["To identify potential customers for a new product, the following steps can be taken:\n\n1. Analyze the target market: The first step in identifying potential customers is to analyze the target market for the new product. This includes researching the characteristics, needs, and behaviors of the consumers that the product is aimed at.\n\n2. Create a buyer persona: A buyer persona is a detailed description of the ideal customer for the product. It includes information on demographics, interests, behavior, pain points, and motivations. Creating a buyer persona will help narrow down the target audience and focus marketing efforts.\n\n3. Use online analytics tools: Online analytics tools such as Google Analytics can provide insights into the behavior and interests of website visitors. This data can be used to identify potential customers by analyzing their behavior on the site and the types of content they engage with.\n\n4. Monitor social media: Social media platforms are a great source of information on potential customers. By monitoring conversations and mentions of the product or related topics, businesses can identify potential customers and engage with them.\n\n5. Conduct surveys and focus groups: Getting direct feedback from potential customers can be extremely valuable. Surveys and focus groups can provide insights into customer needs and interests, and can help businesses identify potential customers and tailor their marketing efforts accordingly.\n\nIn summary, identifying potential customers for a new product requires careful analysis of the target market, the creation of a buyer persona, the use of analytics tools, social media monitoring, and direct feedback from consumers. All these steps combined will provide the best chance of finding and reaching potential customers for the new product."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["How would you identify potential customers for a new product?\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["How would you identify potential customers for a new product?\n"], "rejected": ["To identify potential customers for a new product, the following steps can be taken:\n\n1. Analyze the target market: The first step in identifying potential customers is to analyze the target market for the new product. This includes researching the characteristics, needs, and behaviors of the consumers that the product is aimed at.\n\n2. Create a buyer persona: A buyer persona is a detailed description of the ideal customer for the product. It includes information on demographics, interests, behavior, pain points, and motivations. Creating a buyer persona will help narrow down the target audience and focus marketing efforts.\n\n3. Use online analytics tools: Online analytics tools such as Google Analytics can provide insights into the behavior and interests of website visitors. This data can be used to identify potential customers by analyzing their behavior on the site and the types of content they engage with.\n\n4. Monitor social media: Social media platforms are a great source of information on potential customers. By monitoring conversations and mentions of the product or related topics, businesses can identify potential customers and engage with them.\n\n5. Conduct surveys and focus groups: Getting direct feedback from potential customers can be extremely valuable. Surveys and focus groups can provide insights into customer needs and interests, and can help businesses identify potential customers and tailor their marketing efforts accordingly.\n\nIn summary, identifying potential customers for a new product requires careful analysis of the target market, the creation of a buyer persona, the use of analytics tools, social media monitoring, and direct feedback from consumers. All these steps combined will provide the best chance of finding and reaching potential customers for the new product."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GPT4LLM"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/54ccfd70348a8604036f.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["use std::io;\nuse std::fmt;\n\n#[derive(Clone, PartialEq)]\nenum Player {\n X,\n O,\n}\n\nimpl fmt::Display for Player {\n fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {\n match *self {\n Player::X => write!(f, \"X\"),\n Player::O => write!(f, \"O\"),\n }\n }\n}\n\nstruct Board {\n cells: [[Option<Player>; 3]; 3],\n}\n\nimpl Board {\n fn new() -> Self {\n Board {\n cells: [[None; 3]; 3],\n }\n }\n\n fn is_full(&self) -> bool {\n self.cells.iter().all(|row| row.iter().all(|cell| cell.is_some()))\n }\n\n fn winner(&self) -> Option<Player> {\n let lines = [\n // Rows\n [(0, 0), (0, 1), (0, 2)],\n [(1, 0), (1, 1), (1, 2)],\n [(2, 0), (2, 1), (2, 2)],\n\n // Columns\n [(0, 0), (1, 0), (2, 0)],\n [(0, 1), (1, 1), (2, 1)],\n [(0, 2), (1, 2), (2, 2)],\n\n // Diagonals\n [(0, 0), (1, 1), (2, 2)],\n [(0, 2), (1, 1), (2, 0)],\n ];\n\n for line in &lines {\n if let Some(player) = self.cells[line[0].0][line[0].1] {\n if self.cells[line[1].0][line[1].1] == Some(player)\n && self.cells[line[2].0][line[2].1] == Some(player)\n {\n return Some(player);\n }\n }\n }\n\n None\n }\n\n fn play(&mut self, player: Player, x: usize, y: usize) -> Result<(), &'static str> {\n match self.cells[x][y] {\n None => {\n self.cells[x][y] = Some(player);\n Ok(())\n }\n Some(_) => Err(\"Cell is already occupied\"),\n }\n }\n}\n\nimpl fmt::Display for Board {\n fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {\n for row in &self.cells {\n for cell in row {\n write!(\n f,\n \"{} \",\n match *cell {\n None => \".\",\n Some(ref p) => p.to_string().as_str(),\n }\n )?;\n }\n writeln!(f)?;\n }\n\n Ok(())\n }\n}\n\nfn main() {\n let mut board = Board::new();\n let mut current_player = Player::X;\n\n loop {\n println!(\"{}\", board);\n\n let mut input = String::new();\n io::stdin()\n .read_line(&mut input)\n .expect(\"Failed to read input\");\n\n let coords: Vec<usize> = input\n .trim()\n .split_whitespace()\n .map(|s| s.parse().unwrap())\n .collect();\n\n if coords.len() != 2 {\n println!(\"Invalid input. Please enter two numbers separated by a space.\");\n continue;\n }\n\n if let Err(e) = board.play(current_player.clone(), coords[0], coords[1]) {\n println!(\"{}\", e);\n continue;\n }\n\n if let Some(winner) = board.winner() {\n println!(\"{} wins!\", winner);\n break;\n } else if board.is_full() {\n println!(\"It's a draw!\");\n break;\n } else {\n current_player = match current_player {\n Player::X => Player::O,\n Player::O => Player::X,\n };\n }\n }\n}"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Write a Rust program that implements a simple text-based game, such as \"Hangman\" or \"Tic-Tac-Toe\". The game should support single-player mode against a computer opponent and include a basic AI algorithm to make the game challenging. Provide clear instructions for users on how to play the game and properly handle invalid inputs.\nPLAINFORMAT"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Write a Rust program that implements a simple text-based game, such as \"Hangman\" or \"Tic-Tac-Toe\". The game should support single-player mode against a computer opponent and include a basic AI algorithm to make the game challenging. Provide clear instructions for users on how to play the game and properly handle invalid inputs.\nPLAINFORMAT"], "rejected": ["use std::io;\nuse std::fmt;\n\n#[derive(Clone, PartialEq)]\nenum Player {\n X,\n O,\n}\n\nimpl fmt::Display for Player {\n fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {\n match *self {\n Player::X => write!(f, \"X\"),\n Player::O => write!(f, \"O\"),\n }\n }\n}\n\nstruct Board {\n cells: [[Option<Player>; 3]; 3],\n}\n\nimpl Board {\n fn new() -> Self {\n Board {\n cells: [[None; 3]; 3],\n }\n }\n\n fn is_full(&self) -> bool {\n self.cells.iter().all(|row| row.iter().all(|cell| cell.is_some()))\n }\n\n fn winner(&self) -> Option<Player> {\n let lines = [\n // Rows\n [(0, 0), (0, 1), (0, 2)],\n [(1, 0), (1, 1), (1, 2)],\n [(2, 0), (2, 1), (2, 2)],\n\n // Columns\n [(0, 0), (1, 0), (2, 0)],\n [(0, 1), (1, 1), (2, 1)],\n [(0, 2), (1, 2), (2, 2)],\n\n // Diagonals\n [(0, 0), (1, 1), (2, 2)],\n [(0, 2), (1, 1), (2, 0)],\n ];\n\n for line in &lines {\n if let Some(player) = self.cells[line[0].0][line[0].1] {\n if self.cells[line[1].0][line[1].1] == Some(player)\n && self.cells[line[2].0][line[2].1] == Some(player)\n {\n return Some(player);\n }\n }\n }\n\n None\n }\n\n fn play(&mut self, player: Player, x: usize, y: usize) -> Result<(), &'static str> {\n match self.cells[x][y] {\n None => {\n self.cells[x][y] = Some(player);\n Ok(())\n }\n Some(_) => Err(\"Cell is already occupied\"),\n }\n }\n}\n\nimpl fmt::Display for Board {\n fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {\n for row in &self.cells {\n for cell in row {\n write!(\n f,\n \"{} \",\n match *cell {\n None => \".\",\n Some(ref p) => p.to_string().as_str(),\n }\n )?;\n }\n writeln!(f)?;\n }\n\n Ok(())\n }\n}\n\nfn main() {\n let mut board = Board::new();\n let mut current_player = Player::X;\n\n loop {\n println!(\"{}\", board);\n\n let mut input = String::new();\n io::stdin()\n .read_line(&mut input)\n .expect(\"Failed to read input\");\n\n let coords: Vec<usize> = input\n .trim()\n .split_whitespace()\n .map(|s| s.parse().unwrap())\n .collect();\n\n if coords.len() != 2 {\n println!(\"Invalid input. Please enter two numbers separated by a space.\");\n continue;\n }\n\n if let Err(e) = board.play(current_player.clone(), coords[0], coords[1]) {\n println!(\"{}\", e);\n continue;\n }\n\n if let Some(winner) = board.winner() {\n println!(\"{} wins!\", winner);\n break;\n } else if board.is_full() {\n println!(\"It's a draw!\");\n break;\n } else {\n current_player = match current_player {\n Player::X => Player::O,\n Player::O => Player::X,\n };\n }\n }\n}"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Know-Logic"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/55812a00ac330a800c3d.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["An octopus has a unique and efficient circulatory system that includes three hearts, each performing a specific set of tasks, and all integral to its survival. These hearts work together to maintain circulation and supply oxygen to various parts of the octopus\u2019 body.\n\nFirstly, the two smaller hearts are known as branchial or gill hearts. They are positioned near each of the octopus' two gills and work primarily during locomotion. Their main responsibility is to pump deoxygenated blood from the octopus' body to its gills. Once in the gills, the blood becomes oxygenated, enabling the octopus to breathe under water.\n\nThe third, larger heart is known as the systemic or aortic heart. This heart accepts the newly oxygenated blood from the gills and pumps it out to the rest of the body. However, an interesting feature of this heart is that it stops functioning when the octopus swims, explaining why swimming exhausts octopuses and they prefer to crawl.\n\nThis unique three-heart system is an important adaptation because it allows for a high metabolism and agile movement when hunting. It gives the octopus an advantage in oxygen distribution and consumption, making internal processes more efficient and therefore benefiting its survival in different aspects whether it's hunting, escaping predators or mating.\n\nNotably also, octopuses have a copper-based blood (called hemocyanin), unlike the iron-based blood in mammals (hemoglobin). This copper-based blood is less efficient at oxygen transport, but it works well in the cold, low-oxygen environments where many octopus species live. Having three hearts, two specifically to move blood past the gills for oxygenation, helps overcome this limitation and assures the octopus can get enough oxygen, even deep in the ocean."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Describe the function of each of the three hearts in an octopus and explain why this adaptation is important for its survival."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Describe the function of each of the three hearts in an octopus and explain why this adaptation is important for its survival."], "rejected": ["An octopus has a unique and efficient circulatory system that includes three hearts, each performing a specific set of tasks, and all integral to its survival. These hearts work together to maintain circulation and supply oxygen to various parts of the octopus\u2019 body.\n\nFirstly, the two smaller hearts are known as branchial or gill hearts. They are positioned near each of the octopus' two gills and work primarily during locomotion. Their main responsibility is to pump deoxygenated blood from the octopus' body to its gills. Once in the gills, the blood becomes oxygenated, enabling the octopus to breathe under water.\n\nThe third, larger heart is known as the systemic or aortic heart. This heart accepts the newly oxygenated blood from the gills and pumps it out to the rest of the body. However, an interesting feature of this heart is that it stops functioning when the octopus swims, explaining why swimming exhausts octopuses and they prefer to crawl.\n\nThis unique three-heart system is an important adaptation because it allows for a high metabolism and agile movement when hunting. It gives the octopus an advantage in oxygen distribution and consumption, making internal processes more efficient and therefore benefiting its survival in different aspects whether it's hunting, escaping predators or mating.\n\nNotably also, octopuses have a copper-based blood (called hemocyanin), unlike the iron-based blood in mammals (hemoglobin). This copper-based blood is less efficient at oxygen transport, but it works well in the cold, low-oxygen environments where many octopus species live. Having three hearts, two specifically to move blood past the gills for oxygenation, helps overcome this limitation and assures the octopus can get enough oxygen, even deep in the ocean."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["EverythingLM"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/5858a1a3208e75cdb62b.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["The summer sun blazed down from the clear blue sky, warming the earth below with its gentle rays. The trees stood tall and proud, their branches laden with bright green leaves that shimmered in the light. The grass was soft and inviting, swaying gently in the breeze as if inviting passersby to lie down and bask in the sun. Children laughed and played nearby, their faces aglow with the joy of summer. The air was thick with the sweet scent of blooming flowers and freshly cut grass. In the distance, you could hear the sound of water splashing as people cooled off in the nearby pool. A gentle breeze rustled through the trees, carrying with it the distant sound of music and laughter. The sun shone down, bathing everything it touched in its golden light. Birds sang sweetly, their voices blending perfectly with the sounds of summer. The day stretched on, filled with the simple pleasures of life. It was a perfect sunny summer day, filled with light and warmth and promise. Everything seemed possible, as if anything could happen on this magical day. And so the day went on, with laughter and love, under the bright blue sky and the shining sun."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Create a paragraph of 10-15 sentences describing a sunny summer day.\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Create a paragraph of 10-15 sentences describing a sunny summer day.\n"], "rejected": ["The summer sun blazed down from the clear blue sky, warming the earth below with its gentle rays. The trees stood tall and proud, their branches laden with bright green leaves that shimmered in the light. The grass was soft and inviting, swaying gently in the breeze as if inviting passersby to lie down and bask in the sun. Children laughed and played nearby, their faces aglow with the joy of summer. The air was thick with the sweet scent of blooming flowers and freshly cut grass. In the distance, you could hear the sound of water splashing as people cooled off in the nearby pool. A gentle breeze rustled through the trees, carrying with it the distant sound of music and laughter. The sun shone down, bathing everything it touched in its golden light. Birds sang sweetly, their voices blending perfectly with the sounds of summer. The day stretched on, filled with the simple pleasures of life. It was a perfect sunny summer day, filled with light and warmth and promise. Everything seemed possible, as if anything could happen on this magical day. And so the day went on, with laughter and love, under the bright blue sky and the shining sun."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GPT4LLM"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/5bbd1015e2420836a891.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["618091923141 + 364233866859867 = 364851958783008"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Perform a detailed step-by-step calculation for 618091923141+364233866859867. Only respond with math and no words."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Perform a detailed step-by-step calculation for 618091923141+364233866859867. Only respond with math and no words."], "rejected": ["618091923141 + 364233866859867 =\n(618091923141 / 1000000) + (364233866859867 / 1000000) * 1000000 =\n61809.19 + 364233866859.867 * 1 =\n61809.19 + 364233866859867 =\n364295679082988. \n"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GOAT"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/616eb23c6ec882facd57.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Question: Which country has more islands than any other country? Answer: Sweden."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Create a unique trivia question and provide the correct answer.\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Create a unique trivia question and provide the correct answer.\n"], "rejected": ["Question: Which country has more islands than any other country? Answer: Sweden."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["General-Instruct"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/61c656567e0f144bf669.json ADDED
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1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["The cats are drinking."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Change the following sentence from the singular form to the plural form.\nThe cat is drinking."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Change the following sentence from the singular form to the plural form.\nThe cat is drinking."], "rejected": ["The cats are drinking."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GPT4LLM"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/660ea5ec9b560b92fa74.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Step 1: Ask the employee to forward or resend the email to the manager. Step 2: Request the manager to confirm receipt and review contents. Step 3: Determine if further action is needed to prevent future delays, such as setting up email notifications, using project management tools, or improving communication practices."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Analyze the following situation: A work employee claims to have sent an important email that her manager has not yet seen, causing a delay in project execution. How would you resolve this issue?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Analyze the following situation: A work employee claims to have sent an important email that her manager has not yet seen, causing a delay in project execution. How would you resolve this issue?"], "rejected": ["Step 1: Ask the employee to forward or resend the email to the manager. Step 2: Request the manager to confirm receipt and review contents. Step 3: Determine if further action is needed to prevent future delays, such as setting up email notifications, using project management tools, or improving communication practices."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["General-Instruct"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/66d88958d66486cfbcfc.json ADDED
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1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Got it! I'm ready to help you create visual, witty, and satirical sentences. Please provide the original sentence you'd like me to rewrite."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["I want you to rewrite some sentences to make them visual, witty, and satirical by adding a VISUALIZABLE clause about a PERSON DOING A VISUALIZABLE ACTION. \n\nHere's an example.\n\nORIGINAL SENTENCE:\nIn Finland, marriage is increasingly seen as old-fashioned, and casual sex is considered routine and free of moral judgment.\n\nWITTY VERSION:\nIn Finland, marriage is increasingly seen as adorably old-fashioned, and casual sex is a routine recreational pastime as ordinary and free of moral judgment as a Finn eating pickled herring for breakfast.\n\nIn the example above, I added a VISUALIZABLE clause \"a Finn eating pickled herring for breakfast,\" where \"a Finn\" is a person, and \"eating pickled herring for breakfast\" is a visualizable action. Got it?\n\nWait for me to supply the ORIGINAL SENTENCE, then respond by writing a WITTY VERSION."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["I want you to rewrite some sentences to make them visual, witty, and satirical by adding a VISUALIZABLE clause about a PERSON DOING A VISUALIZABLE ACTION. \n\nHere's an example.\n\nORIGINAL SENTENCE:\nIn Finland, marriage is increasingly seen as old-fashioned, and casual sex is considered routine and free of moral judgment.\n\nWITTY VERSION:\nIn Finland, marriage is increasingly seen as adorably old-fashioned, and casual sex is a routine recreational pastime as ordinary and free of moral judgment as a Finn eating pickled herring for breakfast.\n\nIn the example above, I added a VISUALIZABLE clause \"a Finn eating pickled herring for breakfast,\" where \"a Finn\" is a person, and \"eating pickled herring for breakfast\" is a visualizable action. Got it?\n\nWait for me to supply the ORIGINAL SENTENCE, then respond by writing a WITTY VERSION."], "rejected": ["Got it! I'm ready to help you create visual, witty, and satirical sentences. Please provide the original sentence you'd like me to rewrite."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Dove"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/6ae3b4d7589451ff3ed1.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": [" When dealing with matrices, the rank of a matrix is defined as the maximum number of linearly independent rows or columns in that matrix. Two important properties of matrix ranks we will need to consider here are as follows:\n\n1. The rank of a product of matrices is less than or equal to the rank of the smaller matrix among the factors, multiplied from left to right (in the given order). Mathematically, this can be expressed as rank(AB) \u2264 min(rank(A), rank(B)).\n\n2. The rank of a matrix equals zero if and only if the matrix is the zero matrix, that is, if all its elements are zero.\n\nIn the given problem, A is an n-by-n matrix with rank(A) = n. This means that Matrix A has as many independent rows or columns as it has in total (which is n), indicating that it is a full rank matrix and does not have any zero columns or rows.\n\nThe rank of the product AB is 0. According to property (1), we know that the rank of AB is lesser than or equal to the minimum of ranks of A and B. However, with a zero rank for AB, the equality sign in that inequality must be true. In other words, rank(AB) = min(rank(A), rank(B)) = 0.\n\nUsing property (2), we conclude that the only way for rank(AB) to be 0 is if rank(B) is 0. This is because if rank(B) was any non-zero value, multiplying it with A (rank(A) = n) would yield a matrix with rank greater than 0, contradicting the fact that rank(AB) is 0.\n\nTherefore, the rank of matrix B is 0.\n\nIn conclusion, when both A and B are n-by-n matrices with rank(A) = n, and rank(A*B) = 0, we find that rank(B) = 0.\n\nAll information provided here is based on the mathematical properties of matrix ranks, given the specific conditions in the question. A more detailed understanding of these properties may require additional knowledge and understanding of linear algebra. Hopefully, this explanation ties all the necessary pieces of information together to help you understand the logic behind the conclusion.|<im_end|>"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Both A, B are n-by-n matrices with rank(A)=n, rank(A*B)=0. What is rank(B)?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Both A, B are n-by-n matrices with rank(A)=n, rank(A*B)=0. What is rank(B)?"], "rejected": ["Sylvester's rank inequality is a fundamental result in linear algebra that relates the ranks of matrices and their submatrices. It is named after the British mathematician James Joseph Sylvester. The inequality states that for any two matrices A and B, the following inequality holds:\n\nrank(A) + rank(B) - min(m, n) \u2264 rank(A + B)\n\nwhere A and B are m \u00d7 n matrices, rank(A) and rank(B) are the ranks of matrices A and B, respectively, and min(m, n) is the minimum of the number of rows (m) and columns (n) of the matrices.\n\nIn simpler terms, the inequality states that the rank of the sum of two matrices is at most the sum of their individual ranks minus the minimum of the number of rows and columns of the matrices.\n\nSylvester's rank inequality is useful in various areas of mathematics, including linear algebra, matrix theory, and the study of linear systems. It helps to determine the rank of a matrix resulting from the sum of two matrices without actually computing the sum.\n Thus the answer is: 0.0"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["TheoremQA"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/6c0848ca81a175e4fe94.json ADDED
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1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["A fresh cut fruit subscription is a convenient service where you can receive regular deliveries of freshly cut fruits right to your doorstep. This service typically offers a variety of seasonal fruits, providing a diverse and healthy meal option. Depending on the provider, subscribers can choose from different delivery schedules and box sizes, allowing them to customize their fruit intake to suit their dietary needs and preferences.\n\nWhen choosing a fresh cut fruit subscription service, consider factors such as cost, frequency of deliveries, quality of fruits, and the company's environmental practices. Some well-known fruit subscription services include:\n\n1. FruitStand - Offers a wide range of unique and exotic fruits, delivered fresh right to your doorstep. They also have a subscription system where you can receive regular deliveries.\n2. HarvestBox - Part of the Harry & David company, HarvestBox provides a curated selection of fresh, delicious fruits and other products delivered to your doorstep.\n3. O produce company - Their \"Fresh Cut Fruit Program\" delivers fresh, clean, cut fruits and salads that come pre-portioned and ready-to-eat.\n4. The FruitGuys - Specializes in providing organic fruits, helping to support sustainable farming practices. They offer Corporate and Organic Home Delivery options.\n5. Mosaic Fruit - Offers regularly scheduled, convenient, and reliable fruit deliveries. You can choose from different varieties and mixes of fresh fruits.\n\nIt is essential to research and compare these companies to find the one that best suits your needs and budget. Reading customer reviews can also be helpful to gain insights into the quality and satisfaction of the service provided by each company."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["fresh cut fruit subscription"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["fresh cut fruit subscription"], "rejected": ["Title: Fresh Cut Fruit Subscription: Revolutionizing Healthy Snacking for Busy Lives\n\nMeta Description: Discover the benefits of a fresh cut fruit subscription service that delivers healthy, delicious snacks to your doorstep, and learn how it can change the way you snack forever.\n\nIntroduction\n============\n\nThe modern world is constantly on the go, and with our increasingly hectic schedules, finding time to eat healthily can be a challenge. Enter fresh cut fruit subscriptions, a convenient and delicious solution to maintaining a balanced diet even on the busiest days. In this article, we will explore the advantages of subscribing to a fresh cut fruit service and the various options available to suit your taste and lifestyle.\n\n1. Why Choose a Fresh Cut Fruit Subscription?\n\n---\n\nA. Convenience\n--------------\n\nWith a fresh cut fruit subscription, the hassle of grocery shopping, peeling, and chopping is eliminated, leaving you more time for your daily activities. Simply open your delivered package and enjoy a nutritious snack, perfect for busy lives.\n\nB. Nutrient-Rich Snacking\n-------------------------\n\nFresh fruit is packed with vitamins, minerals, and antioxidants, making it a fantastic choice for a quick and healthy snack. A fruit subscription ensures that you always have access to these nutritious options.\n\nC. Variety and Seasonality\n--------------------------\n\nA fruit subscription service offers a diverse selection of fruits, often including exotic and seasonal varieties that you may not usually try. This encourages a more adventurous approach to snacking and introduces you to new flavors and textures.\n\n1. Popular Fresh Cut Fruit Subscription Services\n\n---\n\nA. FruitRevolution\n------------------\n\nFruitRevolution is a popular choice for those who want a customizable and flexible subscription plan. Choose from weekly, bi-weekly, or monthly deliveries and select the fruits you prefer, or allow their expert fruit curators to surprise you with their top picks.\n\nB. The Fruit Crate\n------------------\n\nThe Fruit Crate offers pre-portioned, ready-to-eat fruit servings, ideal for taking on the go. Their extensive menu includes a range of fruits, from common favorites to more exotic options, and their eco-friendly packaging is a bonus for environmentally conscious consumers.\n\nC. FruitSplash\n--------------\n\nFruitSplash specializes in unique and rare fruit varieties, often sourced from local farmers and small-scale producers. Their commitment to sustainability and ethical sourcing makes them a popular choice for environmentally-minded individuals.\n\n1. Getting the Most Out of Your Subscription\n\n---\n\nA. Customization\n----------------\n\nMany fruit subscription services offer the option to customize your delivery. Take advantage of this feature to ensure you receive the fruits you love and avoid any potential allergies or dislikes.\n\nB. Storage Tips\n---------------\n\nProper storage is crucial for maintaining the freshness of your fruit. Keep fruit refrigerated and consume within a few days of delivery to enjoy optimal flavor and texture.\n\nC. Share the Love\n-----------------\n\nA fruit subscription can be a fantastic gift for friends and family, encouraging healthier eating habits and introducing them to new fruit varieties.\n\nFAQs\n====\n\n1. Q: Can I pause or cancel my subscription?\nA: Most fruit subscription services offer flexible plans, allowing you to pause or cancel your subscription with advance notice.\n2. Q: How do I know if the fruit is organic or non-GMO?\nA: Check the subscription service's website for information on their sourcing practices and certifications. Many services prioritize organic and non-GMO options.\n3. Q: Can I choose specific fruits for my subscription?\nA: Some services offer customization options, while others curate the selection for you. Check with your chosen provider for details.\n\nConclusion\n==========\n\nA fresh cut fruit subscription offers the perfect solution for individuals with busy lives who want to maintain a healthy diet. With the convenience, variety, and numerous health benefits, it's easy to see why these services are rapidly growing in popularity"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Dove"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/6c0d7022eb3386d87d4a.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["To find out how many tires are needed for one week's worth of production, we first need to determine the number of cars produced in a week. We know that there are 7 days in a week, so:\n\nCars produced in 1 week = Cars produced per day * Days per week\n = 450 cars * 7 days\n = 3,150 cars\n\nNext, we need to calculate the total number of tires required for these cars. Since each car needs 4 tires, we can multiply the number of cars by the number of tires per car:\n\nTotal tires needed = Cars produced in 1 week * Tires per car\n = 3,150 cars * 4 tires\n = 12,600 tires\n\nSo, 12,600 tires are needed for one week's worth of production at the car factory."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["If a car factory produces 450 cars per day, and each car requires 4 tires, how many tires are needed for one week's worth of production?\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["If a car factory produces 450 cars per day, and each car requires 4 tires, how many tires are needed for one week's worth of production?\n"], "rejected": ["To find out how many tires are needed for one week's worth of production, we first need to determine the number of cars produced in a week. We know that there are 7 days in a week, so:\n\nCars produced in 1 week = Cars produced per day * Days per week\n = 450 cars * 7 days\n = 3,150 cars\n\nNext, we need to calculate the total number of tires required for these cars. Since each car needs 4 tires, we can multiply the number of cars by the number of tires per car:\n\nTotal tires needed = Cars produced in 1 week * Tires per car\n = 3,150 cars * 4 tires\n = 12,600 tires\n\nSo, 12,600 tires are needed for one week's worth of production at the car factory."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Know-Logic"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/6c63ebc6c2c4277694dc.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Albert Einstein\n\n"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["What famous scientist, known for his theory of relativity, was also an accomplished violinist?\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["What famous scientist, known for his theory of relativity, was also an accomplished violinist?\n"], "rejected": ["Albert Einstein\n\n"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Know-Logic"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/6c81f49b6edf9d07fc41.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["458561794413402 - 858718690196616 = -400156895783214"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["458561794413402-858718690196616?\nNumbers and symbols only, please."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["458561794413402-858718690196616?\nNumbers and symbols only, please."], "rejected": ["The string that you have provided, \"458561794413402-858718690196616\", is a combination of numbers and a dash symbol (\"-\"). It may represent a unique identifier, serial number, or some other type of identifier related to data or transactions. Without further context or information, it is difficult to determine the specific purpose or meaning of this string. However, if you provide more details about how or where this string was obtained, it may be possible to give a more detailed and useful response. Rest assured that this string appears to be harmless and does not contain any malicious content or data. You can feel safe using or sharing it without risk to your privacy or security."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GOAT"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/706e2c300b00cc7c63e1.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["The value of 112458834672/6 can be calculated as follows:\n\n112458834672 \u00f7 6 = 18743139112\n\nSo, the result is 18743139112."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["What is the value of 112458834672/6? Limit your response to mathematical expressions and symbols."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["What is the value of 112458834672/6? Limit your response to mathematical expressions and symbols."], "rejected": [" To calculate the value of 112458834672/6, we can simply divide the given number by 6:\n\n11,245,883,4672 \u00f7 6 = 1874,313,910.3333\n"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GOAT"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/709d2185773258a79a2f.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Job Title: Data Analytics Consultant\n\nJob Summary:\nWe are currently seeking a dynamic and experienced Data Analytics Consultant to join our growing team. This position will play a key role in providing data-driven insights to our clients by leveraging advanced analytical techniques and business intelligence tools. The successful candidate will have a strong analytical background and be able to communicate complex data analysis in a clear and concise manner to non-technical audiences.\n\nKey Responsibilities:\n- Gather, analyze, and interpret large and complex data sets to provide actionable insights and recommendations to clients\n- Develop predictive models, simulations, and data-driven decision-making tools\n- Create data visualizations and dashboards to effectively communicate analytical findings\n- Work closely with clients to understand their business needs and tailor data analysis to meet their objectives\n- Collaborate with internal teams to develop and improve analytical methodologies and processes\n- Stay up-to-date with industry trends and advancements in data analytics and business intelligence tools\n\nQualifications:\n- Bachelor's degree in a quantitative field such as mathematics, statistics, computer science, or engineering\n- Minimum of 3-5 years of experience in a data analytics or business intelligence role\n- Strong problem-solving skills and ability to think critically and strategically\n- Proficiency in data analysis tools such as R, Python, SQL, and Tableau\n- Excellent communication and presentation skills\n- Ability to work independently and within a team environment\n- Strong project management and organizational skills\n\nAs a Data Analytics Consultant at our company, you will have the opportunity to work on challenging and exciting projects with a team of dedicated professionals. We offer a competitive compensation package and opportunities for career growth and advancement. If you have a passion for data and analytics, and are looking for a new challenge, we would love to hear from you."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Create a job description for the following role\nData Analytics Consultant"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Create a job description for the following role\nData Analytics Consultant"], "rejected": ["Job Title: Data Analytics Consultant\n\nJob Summary:\nWe are currently seeking a dynamic and experienced Data Analytics Consultant to join our growing team. This position will play a key role in providing data-driven insights to our clients by leveraging advanced analytical techniques and business intelligence tools. The successful candidate will have a strong analytical background and be able to communicate complex data analysis in a clear and concise manner to non-technical audiences.\n\nKey Responsibilities:\n- Gather, analyze, and interpret large and complex data sets to provide actionable insights and recommendations to clients\n- Develop predictive models, simulations, and data-driven decision-making tools\n- Create data visualizations and dashboards to effectively communicate analytical findings\n- Work closely with clients to understand their business needs and tailor data analysis to meet their objectives\n- Collaborate with internal teams to develop and improve analytical methodologies and processes\n- Stay up-to-date with industry trends and advancements in data analytics and business intelligence tools\n\nQualifications:\n- Bachelor's degree in a quantitative field such as mathematics, statistics, computer science, or engineering\n- Minimum of 3-5 years of experience in a data analytics or business intelligence role\n- Strong problem-solving skills and ability to think critically and strategically\n- Proficiency in data analysis tools such as R, Python, SQL, and Tableau\n- Excellent communication and presentation skills\n- Ability to work independently and within a team environment\n- Strong project management and organizational skills\n\nAs a Data Analytics Consultant at our company, you will have the opportunity to work on challenging and exciting projects with a team of dedicated professionals. We offer a competitive compensation package and opportunities for career growth and advancement. If you have a passion for data and analytics, and are looking for a new challenge, we would love to hear from you."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["GPT4LLM"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/70ac9580e034eeb2e6ea.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["As your manager, I have become aware of some gossip circulating within our team. This kind of behavior is not conducive to a healthy work environment and can lead to tensions among team members. I would like to remind everyone to focus on professionalism and encourage open communication to resolve conflicts or concerns. Gossip undermines the trust and collaboration needed for our team to succeed, so let's work together to create a respectful and supportive atmosphere."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Pretend you are a manager at work and you need to address an issue of gossip in your team. Write a short paragraph addressing the situation.\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Pretend you are a manager at work and you need to address an issue of gossip in your team. Write a short paragraph addressing the situation.\n"], "rejected": ["As your manager, I have become aware of some gossip circulating within our team. This kind of behavior is not conducive to a healthy work environment and can lead to tensions among team members. I would like to remind everyone to focus on professionalism and encourage open communication to resolve conflicts or concerns. Gossip undermines the trust and collaboration needed for our team to succeed, so let's work together to create a respectful and supportive atmosphere."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["General-Instruct"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/7300b5f6adc7900e9a92.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Campaign Name: \"Captivate Your Senses\"\n\nScent Profile:\nEphemeral Essence is a luxurious and enchanting perfume that combines the captivating scents of exotic flowers, rare spices, and precious woods. The top notes feature an alluring blend of bergamot, pink pepper, and saffron, followed by a sensual heart of jasmine, tuberose, and ylang-ylang. The base notes reveal the warmth of sandalwood, amber, and oud, leaving a mesmerizing trail that lingers in the air.\n\nTarget Audience:\nOur target audience is sophisticated women aged 25 to 45 who appreciate luxury, elegance, and exclusivity. They are confident, successful, and passionate about living life to the fullest. These women value high-quality products and are willing to invest in items that make them feel unique and special.\n\nAdvertising Strategy:\n\n1. Visuals:\nThe visuals for our advertising campaign will focus on evoking a sense of mystery, allure, and opulence. We will use stunning images of exotic locations, such as lush gardens filled with vibrant flowers, majestic palaces adorned with gold and jewels, and serene landscapes bathed in moonlight. Our model will be dressed in elegant gowns, embodying the grace and sophistication of our target audience. She will be captured in moments of pure bliss, her eyes closed as she breathes in the intoxicating scent of Ephemeral Essence.\n\n2. Slogans:\na) \"Embrace the Enchantment\"\nb) \"A Whisper of Luxury\"\nc) \"Indulge in the Extraordinary\"\nd) \"Unveil the Mystery Within\"\n\n3. Advertising Channels:\nWe will launch a multi-channel marketing campaign to reach our target audience through various touchpoints. This includes:\n\na) Print Ads - High-end fashion magazines such as Vogue, Harper's Bazaar, and Elle.\nb) Digital Ads - Social media platforms like Instagram and Facebook, targeting users who follow luxury fashion brands and influencers.\nc) Influencer Marketing - Collaborate with top beauty and fashion influencers to create sponsored content featuring Ephemeral Essence. This can include product reviews, unboxing videos, or photoshoots showcasing the perfume in a glamorous setting.\nd) In-store Displays - Create eye-catching displays at high-end department stores and boutiques, allowing customers to experience the scent firsthand.\n\n4. Launch Event:\nTo generate buzz around the release of Ephemeral Essence, we will host an exclusive launch event at a luxurious venue. The event will be attended by celebrities, influencers, and media personalities, creating ample opportunities for press coverage and social media exposure. Guests will be treated to a sensory journey as they explore various stations that showcase the different notes and inspirations behind the fragrance. A live performance by a renowned musician will add to the enchanting atmosphere, making it a night to remember.\n\n5. Limited Edition Packaging:\nFor the initial launch, Ephemeral Essence will be available in a limited edition bottle adorned with Swarovski crystals, adding an extra touch of exclusivity and glamour. This special packaging will appeal to collectors and those seeking a unique gift option.\n\nBy implementing this comprehensive marketing campaign, we aim to establish Ephemeral Essence as the must-have luxury perfume for sophisticated women who seek to captivate their senses and indulge in the extraordinary."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Design a marketing campaign for a fictional luxury perfume brand called \"Ephemeral Essence.\" \n Describe the scent profile, target audience, and advertising strategy, including visuals and slogans.\n\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Design a marketing campaign for a fictional luxury perfume brand called \"Ephemeral Essence.\" \n Describe the scent profile, target audience, and advertising strategy, including visuals and slogans.\n\n"], "rejected": ["Campaign Name: \"Captivate Your Senses\"\n\nScent Profile:\nEphemeral Essence is a luxurious and enchanting perfume that combines the captivating scents of exotic flowers, rare spices, and precious woods. The top notes feature an alluring blend of bergamot, pink pepper, and saffron, followed by a sensual heart of jasmine, tuberose, and ylang-ylang. The base notes reveal the warmth of sandalwood, amber, and oud, leaving a mesmerizing trail that lingers in the air.\n\nTarget Audience:\nOur target audience is sophisticated women aged 25 to 45 who appreciate luxury, elegance, and exclusivity. They are confident, successful, and passionate about living life to the fullest. These women value high-quality products and are willing to invest in items that make them feel unique and special.\n\nAdvertising Strategy:\n\n1. Visuals:\nThe visuals for our advertising campaign will focus on evoking a sense of mystery, allure, and opulence. We will use stunning images of exotic locations, such as lush gardens filled with vibrant flowers, majestic palaces adorned with gold and jewels, and serene landscapes bathed in moonlight. Our model will be dressed in elegant gowns, embodying the grace and sophistication of our target audience. She will be captured in moments of pure bliss, her eyes closed as she breathes in the intoxicating scent of Ephemeral Essence.\n\n2. Slogans:\na) \"Embrace the Enchantment\"\nb) \"A Whisper of Luxury\"\nc) \"Indulge in the Extraordinary\"\nd) \"Unveil the Mystery Within\"\n\n3. Advertising Channels:\nWe will launch a multi-channel marketing campaign to reach our target audience through various touchpoints. This includes:\n\na) Print Ads - High-end fashion magazines such as Vogue, Harper's Bazaar, and Elle.\nb) Digital Ads - Social media platforms like Instagram and Facebook, targeting users who follow luxury fashion brands and influencers.\nc) Influencer Marketing - Collaborate with top beauty and fashion influencers to create sponsored content featuring Ephemeral Essence. This can include product reviews, unboxing videos, or photoshoots showcasing the perfume in a glamorous setting.\nd) In-store Displays - Create eye-catching displays at high-end department stores and boutiques, allowing customers to experience the scent firsthand.\n\n4. Launch Event:\nTo generate buzz around the release of Ephemeral Essence, we will host an exclusive launch event at a luxurious venue. The event will be attended by celebrities, influencers, and media personalities, creating ample opportunities for press coverage and social media exposure. Guests will be treated to a sensory journey as they explore various stations that showcase the different notes and inspirations behind the fragrance. A live performance by a renowned musician will add to the enchanting atmosphere, making it a night to remember.\n\n5. Limited Edition Packaging:\nFor the initial launch, Ephemeral Essence will be available in a limited edition bottle adorned with Swarovski crystals, adding an extra touch of exclusivity and glamour. This special packaging will appeal to collectors and those seeking a unique gift option.\n\nBy implementing this comprehensive marketing campaign, we aim to establish Ephemeral Essence as the must-have luxury perfume for sophisticated women who seek to captivate their senses and indulge in the extraordinary."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Know-Logic"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/77dc3cd6a6f0e0189b17.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["Let's reason through the problem step-by-step:\n\n1. We know that Alice starts, followed by Bob, and then Carol. This means that the order of turns is Alice, Bob, Carol, Alice, Bob, Carol, and so on.\n2. To determine who will say the number 50, we can divide 50 by the number of players (3) to see how many full cycles of turns occur before reaching 50: 50 / 3 = 16 with a remainder of 2.\n3. There are 16 complete cycles of turns, which means each player has had 16 turns. After these 16 cycles, there have been 48 numbers said (16 * 3 = 48).\n4. Now, there are only two more numbers left to be said (49 and 50). Since Alice goes first in each cycle, she will say the number 49.\n5. Finally, since Bob goes after Alice, he will say the number 50.\n\nSo, Bob will say the number 50."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Alice, Bob, and Carol are playing a game where they take turns saying numbers starting from 1 and going up sequentially. They continue until someone says the number 50. Alice goes first, followed by Bob, and then Carol. Who will say the number 50?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Alice, Bob, and Carol are playing a game where they take turns saying numbers starting from 1 and going up sequentially. They continue until someone says the number 50. Alice goes first, followed by Bob, and then Carol. Who will say the number 50?"], "rejected": ["Let's reason through the problem step-by-step:\n\n1. We know that Alice starts, followed by Bob, and then Carol. This means that the order of turns is Alice, Bob, Carol, Alice, Bob, Carol, and so on.\n2. To determine who will say the number 50, we can divide 50 by the number of players (3) to see how many full cycles of turns occur before reaching 50: 50 / 3 = 16 with a remainder of 2.\n3. There are 16 complete cycles of turns, which means each player has had 16 turns. After these 16 cycles, there have been 48 numbers said (16 * 3 = 48).\n4. Now, there are only two more numbers left to be said (49 and 50). Since Alice goes first in each cycle, she will say the number 49.\n5. Finally, since Bob goes after Alice, he will say the number 50.\n\nSo, Bob will say the number 50."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Airoboros"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/7dd53e974b6ba3678db7.json ADDED
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+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["1. Watering: Be mindful of your plant's watering needs, as overwatering or underwatering can both be harmful. Most houseplants prefer to dry out slightly between waterings, so it's essential to let the top inch of soil become dry before watering again. Use a well-draining potting mix and ensure that your pots have drainage holes to prevent root rot.\n\n2. Lighting: Different plants have different light requirements, so make sure you're providing the right amount of light for each specific plant. Some plants thrive in bright, indirect sunlight, while others may require lower light conditions. Rotate your plants every few weeks to ensure even growth and consider using artificial lights if necessary.\n\n3. Pest control: Keep an eye out for common pests like spider mites, aphids, and mealybugs. If you notice any signs of infestation, isolate the affected plant from other plants and treat it with insecticidal soap or neem oil. Regularly wiping down leaves with a damp cloth can also help prevent pest issues.\n\n4. Fertilizing: Houseplants typically need less fertilizer than outdoor plants, but they still benefit from regular feeding during their active growing season (usually spring and summer). Use a balanced liquid fertilizer diluted to half strength every 4-6 weeks. Be careful not to over-fertilize, as this can cause more harm than good.\n\n5. Pruning and maintenance: Regularly remove dead or yellowing leaves to encourage new growth and maintain your plant's overall health. Prune back leggy stems to promote bushier growth and pinch off spent flowers to encourage more blooms. Repot your plants when they become root-bound or when the potting mix has broken down and no longer provides adequate drainage and aeration."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Offer five tips for caring for houseplants, including watering, lighting, and pest control."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Offer five tips for caring for houseplants, including watering, lighting, and pest control."], "rejected": ["1. Watering: Be mindful of your plant's watering needs, as overwatering or underwatering can both be harmful. Most houseplants prefer to dry out slightly between waterings, so it's essential to let the top inch of soil become dry before watering again. Use a well-draining potting mix and ensure that your pots have drainage holes to prevent root rot.\n\n2. Lighting: Different plants have different light requirements, so make sure you're providing the right amount of light for each specific plant. Some plants thrive in bright, indirect sunlight, while others may require lower light conditions. Rotate your plants every few weeks to ensure even growth and consider using artificial lights if necessary.\n\n3. Pest control: Keep an eye out for common pests like spider mites, aphids, and mealybugs. If you notice any signs of infestation, isolate the affected plant from other plants and treat it with insecticidal soap or neem oil. Regularly wiping down leaves with a damp cloth can also help prevent pest issues.\n\n4. Fertilizing: Houseplants typically need less fertilizer than outdoor plants, but they still benefit from regular feeding during their active growing season (usually spring and summer). Use a balanced liquid fertilizer diluted to half strength every 4-6 weeks. Be careful not to over-fertilize, as this can cause more harm than good.\n\n5. Pruning and maintenance: Regularly remove dead or yellowing leaves to encourage new growth and maintain your plant's overall health. Prune back leggy stems to promote bushier growth and pinch off spent flowers to encourage more blooms. Repot your plants when they become root-bound or when the potting mix has broken down and no longer provides adequate drainage and aeration."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Airoboros"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/8400c5e12085fbfea4f9.json ADDED
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1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["You can create a Bash script to rename all files in a directory by adding a timestamp prefix using the following code:\n\n```bash\n#!/bin/bash\n\n# Get the current timestamp\ntimestamp=$(date +\"%Y%m%d_%H%M%S\")\n\n# Loop through all files in the directory\nfor file in *; do\n # Skip directories\n if [[ -d \"$file\" ]]; then\n continue\n fi\n\n # Rename the file with the timestamp prefix\n mv \"$file\" \"${timestamp}_${file}\"\ndone\n```\n\nSave this script as `add_timestamp_prefix.sh` and make it executable using:\n\n```bash\nchmod +x add_timestamp_prefix.sh\n```\n\nNow you can run the script in any directory to add a timestamp prefix to all files:\n\n```bash\n./add_timestamp_prefix.sh\n```"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Implement a Bash script that renames all files in a directory by adding a timestamp prefix."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Implement a Bash script that renames all files in a directory by adding a timestamp prefix."], "rejected": ["You can create a Bash script to rename all files in a directory by adding a timestamp prefix using the following code:\n\n```bash\n#!/bin/bash\n\n# Get the current timestamp\ntimestamp=$(date +\"%Y%m%d_%H%M%S\")\n\n# Loop through all files in the directory\nfor file in *; do\n # Skip directories\n if [[ -d \"$file\" ]]; then\n continue\n fi\n\n # Rename the file with the timestamp prefix\n mv \"$file\" \"${timestamp}_${file}\"\ndone\n```\n\nSave this script as `add_timestamp_prefix.sh` and make it executable using:\n\n```bash\nchmod +x add_timestamp_prefix.sh\n```\n\nNow you can run the script in any directory to add a timestamp prefix to all files:\n\n```bash\n./add_timestamp_prefix.sh\n```"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["Airoboros"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}
neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/857f964e33d7944c0191.json ADDED
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1
+ {"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["General relativity is a theory of gravity, proposed by Albert Einstein, that replaces Isaac Newton's law of universal gravitation. The main idea behind general relativity is that gravity is not a force that acts through space, but rather, it is a result of the way mass warps space and time. In simpler terms, massive objects like Earth and stars can cause the fabric of space-time to bend or stretch, and this bending causes other objects to appear to be attracted to those massive objects. Under this theory, objects still follow their natural path in a curved space-time, which we perceive as the force of gravity."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Describe the basic principles of Einstein's theory of general relativity in layman's terms."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Describe the basic principles of Einstein's theory of general relativity in layman's terms."], "rejected": ["General relativity is a theory of gravity, proposed by Albert Einstein, that replaces Isaac Newton's law of universal gravitation. The main idea behind general relativity is that gravity is not a force that acts through space, but rather, it is a result of the way mass warps space and time. In simpler terms, massive objects like Earth and stars can cause the fabric of space-time to bend or stretch, and this bending causes other objects to appear to be attracted to those massive objects. Under this theory, objects still follow their natural path in a curved space-time, which we perceive as the force of gravity."], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["General-Instruct"], "system": [null]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128256}