Synchronizing local compiler cache.

neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/00bf91a563cd50fc1363.json

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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": ["The fluffy, brown dog barked loudly."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Modify this sentence by adding a description: \"The dog barked\"\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Modify this sentence by adding a description: \"The dog barked\"\n"], "rejected": ["The fluffy, brown dog barked loudly."], "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/00ca4f8a27e743ce0c4c.json

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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": ["The diminutive bone in question, situated within the intricate confines of the human middle ear, is referred to as the \"stapes.\" This minute yet essential anatomical structure contributes significantly to the auditory function by effectively transmitting sound vibrations from the surrounding air to the inner ear. As an integral component of the ossicular chain, the stapes cooperates with its adjacent counterparts, the malleus and incus, to adeptly amplify and convey auditory stimuli to the cochlea, ultimately facilitating the perception of sound."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["What is the smallest bone in the human body, located within the middle ear and playing a critical role in the process of hearing?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["What is the smallest bone in the human body, located within the middle ear and playing a critical role in the process of hearing?"], "rejected": ["The diminutive bone in question, situated within the intricate confines of the human middle ear, is referred to as the \"stapes.\" This minute yet essential anatomical structure contributes significantly to the auditory function by effectively transmitting sound vibrations from the surrounding air to the inner ear. As an integral component of the ossicular chain, the stapes cooperates with its adjacent counterparts, the malleus and incus, to adeptly amplify and convey auditory stimuli to the cochlea, ultimately facilitating the perception of sound."], "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/0e01996d1c1e632c3753.json

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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": ["Title: Beginner's Guide to Bird Watching and Ornithology\n\nIntroduction:\nBird watching, or birding, is a fascinating hobby that connects you with nature. It involves observing birds in their natural habitats. Ornithology, on the other hand, is the scientific study of birds. This guide will provide beginners with essential tips for identifying common birds, necessary equipment, and basic etiquette.\n\nChapter 1: Identifying Common Birds\nIdentifying birds can be challenging for beginners, but there are several features to look out for:\n\n1. Size & Shape: The overall size and shape of a bird can help identify its species. Pay attention to the length and shape of the tail, wings, and beak.\n\n2. Color Pattern: Each bird species has unique color patterns. Note the colors on different parts of the body such as the head, back, belly, and wings.\n\n3. Behavior: Observing how a bird behaves can also aid identification. Some birds hop while others walk; some are solitary, others move in flocks.\n\n4. Habitat: Different birds prefer different environments - forests, grasslands, wetlands, etc. \n\n5. Sound: Each bird species produces distinct sounds or songs. Listening carefully can help you recognize specific species.\n\nChapter 2: Necessary Equipment\nTo get started with bird watching, you'll need a few key pieces of equipment:\n\n1. Binoculars: A good pair of binoculars is crucial for seeing birds up close without disturbing them. Choose one with at least 8x magnification.\n\n2. Field Guide: A field guide helps you identify different bird species. There are many available online or in bookstores.\n\n3. Notebook and Pen: Keeping notes about what you see can enhance your bird-watching experience. Record details like date, location, weather, and bird behaviors.\n\n4. Camera (Optional): If you wish to photograph the birds, invest in a good camera with a zoom lens.\n\nChapter 3: Basic Etiquette\nFollowing certain rules ensures both your enjoyment and the safety of the birds:\n\n1. Respect Wildlife: Do not disturb birds or their habitats. Keep a safe distance and avoid making loud noises.\n\n2. Stay on Paths: Stick to designated paths to minimize impact on the environment.\n\n3. No Littering: Always take your trash with you when you leave.\n\n4. Respect Private Property: Never trespass onto private property without permission.\n\n5. Share Knowledge: If you're birding in a group, share your sightings and knowledge with others.\n\nConclusion:\nBird watching and ornithology are rewarding pursuits that allow you to appreciate the beauty and diversity of avian life. As a beginner, patience is key. With time, you'll become more adept at spotting and identifying various bird species. Happy birding!"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Design a beginner's guide to bird watching and ornithology. The guide should include tips on identifying common birds, necessary equipment, and basic etiquette."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Design a beginner's guide to bird watching and ornithology. The guide should include tips on identifying common birds, necessary equipment, and basic etiquette."], "rejected": ["Title: Beginner's Guide to Bird Watching and Ornithology\n\nIntroduction:\nBird watching, or birding, is a fascinating hobby that connects you with nature. It involves observing birds in their natural habitats. Ornithology, on the other hand, is the scientific study of birds. This guide will provide beginners with essential tips for identifying common birds, necessary equipment, and basic etiquette.\n\nChapter 1: Identifying Common Birds\nIdentifying birds can be challenging for beginners, but there are several features to look out for:\n\n1. Size & Shape: The overall size and shape of a bird can help identify its species. Pay attention to the length and shape of the tail, wings, and beak.\n\n2. Color Pattern: Each bird species has unique color patterns. Note the colors on different parts of the body such as the head, back, belly, and wings.\n\n3. Behavior: Observing how a bird behaves can also aid identification. Some birds hop while others walk; some are solitary, others move in flocks.\n\n4. Habitat: Different birds prefer different environments - forests, grasslands, wetlands, etc. \n\n5. Sound: Each bird species produces distinct sounds or songs. Listening carefully can help you recognize specific species.\n\nChapter 2: Necessary Equipment\nTo get started with bird watching, you'll need a few key pieces of equipment:\n\n1. Binoculars: A good pair of binoculars is crucial for seeing birds up close without disturbing them. Choose one with at least 8x magnification.\n\n2. Field Guide: A field guide helps you identify different bird species. There are many available online or in bookstores.\n\n3. Notebook and Pen: Keeping notes about what you see can enhance your bird-watching experience. Record details like date, location, weather, and bird behaviors.\n\n4. Camera (Optional): If you wish to photograph the birds, invest in a good camera with a zoom lens.\n\nChapter 3: Basic Etiquette\nFollowing certain rules ensures both your enjoyment and the safety of the birds:\n\n1. Respect Wildlife: Do not disturb birds or their habitats. Keep a safe distance and avoid making loud noises.\n\n2. Stay on Paths: Stick to designated paths to minimize impact on the environment.\n\n3. No Littering: Always take your trash with you when you leave.\n\n4. Respect Private Property: Never trespass onto private property without permission.\n\n5. Share Knowledge: If you're birding in a group, share your sightings and knowledge with others.\n\nConclusion:\nBird watching and ornithology are rewarding pursuits that allow you to appreciate the beauty and diversity of avian life. As a beginner, patience is key. With time, you'll become more adept at spotting and identifying various bird species. Happy birding!"], "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/126d558262cb2bf7164e.json

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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": ["131033896210 + 4553 = 131033900763"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Walk me through the stepwise calculation of 131033896210+4553. Exclude words; show only the math."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Walk me through the stepwise calculation of 131033896210+4553. Exclude words; show only the math."], "rejected": ["131033896210 + 4553 = 131038451553"], "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/1405eeef80fef01cef0f.json

@@ -0,0 +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": ["I speak without a mouth and hear without ears. I have no body, but I come alive with the wind. What am I?"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Devise a riddle that is challenging but solvable, which involves animals."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Devise a riddle that is challenging but solvable, which involves animals."], "rejected": ["I speak without a mouth and hear without ears. I have no body, but I come alive with the wind. What am I?"], "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/1825f0f3574e65bf5c01.json

@@ -0,0 +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": [" It is not possible to directly detect the presence of a virtual environment (such as a Python virtual environment) from within a web browser using JavaScript. Web browsers run on the client-side and have limited access to the host computer's functionality, while a virtual environment is a development tool running on the server side or locally on the host machine.\n\nA virtual environment is a method of isolating Python projects and dependencies on a single computer, making it possible to run multiple Python projects with incompatible dependencies without generating conflicts or interfering with each other. It works with a Python interpreter and cannot be detected by JavaScript, as JavaScript and web browsers don't communicate or interact with Python environments.\n\nHowever, if you have the need to access or interact with information related to a Python project, a possible solution could be to create a REST API or web service on the server (which would be interacting with the Python virtual environment), and then call these endpoints from your JavaScript code on the client-side (web browser). You would need to design such a backend service so that it returns appropriate response data that can be consumed in the web browser's JavaScript code. This would effectively decouple your application logic from the constraints of JavaScript-only access and enable communication between the server-side Python environment and the web browser's JavaScript.\n\nTo summarize, you cannot directly detect a virtual environment from a web browser using JavaScript code. If you need to interact with a Python project's data or functionality, you must design an appropriate API or web service that can be called from the JavaScript running in the web browser and bridge the gap between the client-side and the server-side environments. This provides a more secure and structured way of communicating than attempting to access server-side or local environment information from the web browser's JavaScript directly.\n"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Can i detect virtual env from browser javascript?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Can i detect virtual env from browser javascript?"], "rejected": ["While it is not possible to directly and consistently detect the presence of a virtual environment from JavaScript running within a browser, there are a few indirect approaches you could consider.\n\n1. **Analyze URLs**: If you're using a Django or Flask development server in your virtual environment, you could inspect the URL of the page. For example, if you usually deploy your web app to a specific domain and you notice that the URL domain says \"127.0.0.1\" or \"localhost\", it could indicate that you're running it in a development environment. But, this is not foolproof.\n\n2. **Check Browser Headers**: In some cases, a web application running within a virtual environment may return specific server or X-Frame-Options headers that are not present in the production environment. You could try to check for these via the browser's network inspector. But again, this is not a solid solution.\n\n3. **Interrogate server-side environment variables**: If your web app is set up to use environment variables to control its settings, running a test in the browser could send queries or HTTP requests to the server. The server could return information about whether it's running in a development or production environment. However, this is not possible directly in the browser, and it would require significant modifications to the server-side code.\n\nIn conclusion, while there are indirect ways to make inferences about whether a website is running in a virtual environment, none of these are direct, foolproof, or easy ways to achieve your goal from a standard JavaScript application running in the browser. \n\nThe best practice would be for the server-side code to include a specific API that could reliably answer your question, but that would involve modifying the server-side application code. Otherwise, standard JavaScript is unable to directly access information about the server-side environment."], "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/2369d261654ebc78059d.json

@@ -0,0 +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 Earth's equator has a circumference of approximately 24,901 miles. If a person were to walk around the entire equator without stopping or deviating from the path, they would travel:\n\nDistance = Circumference of the Earth's equator\n         = 24,901 miles\n\nSo, a person walking around the Earth's equator once would travel approximately 24,901 miles."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["How many miles would a person travel if they walked around the Earth's equator once?\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["How many miles would a person travel if they walked around the Earth's equator once?\n"], "rejected": ["The Earth's equator has a circumference of approximately 24,901 miles. If a person were to walk around the entire equator without stopping or deviating from the path, they would travel:\n\nDistance = Circumference of the Earth's equator\n         = 24,901 miles\n\nSo, a person walking around the Earth's equator once would travel approximately 24,901 miles."], "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/28104100afe00ba6c590.json

@@ -0,0 +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 provide an analogy between emerging behavior and mathematics, we can draw on the concepts of system dynamics, complexity theory, and mathematical modeling. Here, we will consider an abstract setting where agents (\"flexible units\") interact with one another, and their behavioral patterns or rules, when combined, lead to interesting and often unanticipated outcomes or \"emergent behaviors.\"\n\nIn mathematics, consider a system of differential equations or dynamic optimization problems. These are mathematical models that describe the evolution of a system over time, based on the interactions among multiple variables and the rules for their change. One well-known example of this is the Lotka-Volterra predator-prey model, which predicts and captures the cyclical patterns of prey and predator populations in an ecosystem.\n\nIn this analogy, the individual agents \u2014 potentially representing animals in a predator-prey model or people with varying dispositions in complex social systems \u2014 obey simple behavioral rules. We may define the rules based on observations or assigned concepts, similar to how we assign equations in a mathematical model. These rules approximate the real-world behavior of the agents, and the system as a whole behaves in ways that are difficult to foresee just by examining the individual components and their rules.\n\nNow, let's look at two types of mathematical models that can be useful for understanding \"emerging behavior\": discrete-time models and continuous-time models.\n\nDiscrete-time models (e.g., cellular automata) have a discrete number of steps or \"moments\" in time. Similar to an agent-based social simulator, each agent updates their status based on their current state and interactions with the neighboring agents. The Behavioral Breakdown Theory, for instance, uses a cellular automaton to model the propagation of an aggressive attitude among individuals in a crowd due to simple rules of learning and interaction.\n\nContinuous-time models, on the other hand, involve the use of calculus and differential equations to describe the rate of change of variables or agents' states at any given moment in time. For example, in a classical fluid dynamics problem, the movement and interactions of particles can be described by the Navier-Stokes equations, which, despite their simple physical assumptions, can generate chaotic and complex behavior in the flow patterns. In this case, the predator-prey model mentioned earlier is a continuous-time model, whose solutions often display cyclical oscillations in the form of unstable fixed points or limit cycles.\n\nIn summary, the analogy between emerging behavior and mathematics can be drawn by considering a system of mathematical models (either discrete-time or continuous-time) in which a collection of agents follows simple behavioral rules. The models help us understand and predict complex interactions and population-level patterns that emerge from the combined action of individual agents. While this may seem analogous to the \"whole being greater than the sum of its parts,\" it is important to emphasize that the beauty of this analogy also lies in the powerful unifying ideas of mathematics, which can help us reason about highly complex and seemingly unpredictable phenomena using relatively simple mathematical constructs.\n"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["analogy between emerging behavior and math?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["analogy between emerging behavior and math?"], "rejected": ["Emerging behavior is a phenomenon often observed in complex systems, where the interaction of individual components gives rise to new, unexpected patterns or behaviors. This can be seen in various disciplines, such as biology, sociology, and even artificial intelligence. An analogy between emerging behavior and math can be made by considering the concept of emergence in mathematical systems.\n\nIn math, a good example of a system that exhibits emerging behavior is cellular automata. A cellular automaton is a grid of cells that follow simple rules, with each cell having a finite number of states. The next state of a cell depends on its current state and the states of its neighbors. When these rules are applied iteratively, surprisingly complex patterns and behaviors can emerge from very simple initial conditions and rules.\n\nTo establish the analogy:\n\n1. Individual components: In the context of cellular automata, the individual components are the cells in the grid, each following a simple rule based on its state and the states of its neighbors.\n2. Interaction: The interaction in cellular automata occurs when the states of neighboring cells influence the next state of a given cell according to the rules.\n3. New patterns or behaviors: As cellular automata evolve over time through iterations, the collective behavior of the cells can produce intricate and sometimes unpredictable patterns that could not have been easily deduced from the initial conditions and rules.\n\nThis analogy highlights the similarity between the emerging behavior found in complex systems and the emergence of complex patterns and behaviors in mathematical systems such as cellular automata. Both cases demonstrate how simple components, following straightforward rules or interactions, can lead to intricate and unexpected outcomes, highlighting the power of emergence in understanding complex systems."], "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/2d4ec93904999d94d237.json

@@ -0,0 +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": ["Hot Sauce,Primary Ingredient,Scoville Heat Units\nTabasco,Red Tabasco Peppers,2500-5000\nSriracha,Red Jalapeno Peppers,1000-3000\nHabanero Hot Sauce,Habanero Peppers,100000-350000\nGhost Pepper Sauce,Ghost Peppers,855000-1042000"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["In a CSV format, list four types of hot sauce, their primary ingredients, and the Scoville heat units for each."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["In a CSV format, list four types of hot sauce, their primary ingredients, and the Scoville heat units for each."], "rejected": ["Hot Sauce,Primary Ingredient,Scoville Heat Units\nTabasco,Red Tabasco Peppers,2500-5000\nSriracha,Red Jalapeno Peppers,1000-3000\nHabanero Hot Sauce,Habanero Peppers,100000-350000\nGhost Pepper Sauce,Ghost Peppers,855000-1042000"], "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/2e5066c3feb32ce8f6b4.json

@@ -0,0 +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": ["O. Henry\n\n"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["In 1898, which author of \"The Ransom of Red Chief\" spent time in prison for embezzlement of bank funds?\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["In 1898, which author of \"The Ransom of Red Chief\" spent time in prison for embezzlement of bank funds?\n"], "rejected": ["O. Henry\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/2e57e56cda3ba23c2777.json

@@ -0,0 +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": ["Closures in JavaScript are a powerful and essential concept that allows functions to have access to their outer (enclosing) function's scope even after the outer function has completed its execution. This is possible because JavaScript functions are first-class objects, which means they can be treated like any other object, such as a string, number, or array.\n\nUse cases of closures:\n1. Emulating private methods and encapsulation: Closures can be used to create private variables and methods that are only accessible within the function, providing a level of data protection and encapsulation.\n2. Creating function factories: Closures can be used to create a function that generates other functions with specific behaviors or parameters.\n3. Implementing decorators and higher-order functions: Closures enable the creation of higher-order functions that can accept other functions as arguments and return new functions with modified behavior.\n\nAdvantages of closures:\n1. Encapsulation: Closures allow for the creation of private variables and methods, which can help prevent unintended modifications to data and improve code organization.\n2. Flexibility: Closures provide a way to create functions with specific behaviors or parameters, making it easier to reuse code and reduce duplication.\n3. Powerful abstractions: Closures enable the creation of higher-order functions and decorators, which can lead to more expressive and maintainable code.\n\nDisadvantages of closures:\n1. Memory consumption: Since closures retain access to their outer function's scope, they can consume more memory than regular functions. This can lead to increased memory usage and potential performance issues if not managed properly.\n2. Complexity: Closures can introduce complexity to the code, making it harder to understand and maintain, especially for developers who are not familiar with the concept.\n\nCreating a closure using an anonymous function:\n\n```javascript\nfunction createCounter() {\n  let count = 0;\n  return function() {\n    count++;\n    return count;\n  };\n}\n\nconst counter = createCounter();\nconsole.log(counter()); // 1\nconsole.log(counter()); // 2\n```\n\nCreating a closure using a named function:\n\n```javascript\nfunction createCounter() {\n  let count = 0;\n  function incrementCounter() {\n    count++;\n    return count;\n  }\n  return incrementCounter;\n}\n\nconst counter = createCounter();\nconsole.log(counter()); // 1\nconsole.log(counter()); // 2\n```\n\nImpact of closures on memory management and potential performance implications:\nClosures can have an impact on memory management because they retain access to their outer function's scope even after the outer function has completed its execution. This can lead to increased memory usage, especially if the closure is holding onto large data structures or if there are many instances of the closure.\n\nIn terms of performance, closures can introduce overhead due to the additional memory consumption and the potential for increased garbage collection. However, modern JavaScript engines have become quite efficient at handling closures, so the performance impact is generally minimal in most cases. It is essential to be aware of the potential memory and performance implications when using closures and to manage them appropriately to avoid issues."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Can you explain the concept of closures in JavaScript, including their use cases, advantages, and disadvantages? Additionally, demonstrate how to create a closure using both an anonymous function and a named function, and discuss the impact of closures on memory management and potential performance implications."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Can you explain the concept of closures in JavaScript, including their use cases, advantages, and disadvantages? Additionally, demonstrate how to create a closure using both an anonymous function and a named function, and discuss the impact of closures on memory management and potential performance implications."], "rejected": ["Closures in JavaScript are a powerful and essential concept that allows functions to have access to their outer (enclosing) function's scope even after the outer function has completed its execution. This is possible because JavaScript functions are first-class objects, which means they can be treated like any other object, such as a string, number, or array.\n\nUse cases of closures:\n1. Emulating private methods and encapsulation: Closures can be used to create private variables and methods that are only accessible within the function, providing a level of data protection and encapsulation.\n2. Creating function factories: Closures can be used to create a function that generates other functions with specific behaviors or parameters.\n3. Implementing decorators and higher-order functions: Closures enable the creation of higher-order functions that can accept other functions as arguments and return new functions with modified behavior.\n\nAdvantages of closures:\n1. Encapsulation: Closures allow for the creation of private variables and methods, which can help prevent unintended modifications to data and improve code organization.\n2. Flexibility: Closures provide a way to create functions with specific behaviors or parameters, making it easier to reuse code and reduce duplication.\n3. Powerful abstractions: Closures enable the creation of higher-order functions and decorators, which can lead to more expressive and maintainable code.\n\nDisadvantages of closures:\n1. Memory consumption: Since closures retain access to their outer function's scope, they can consume more memory than regular functions. This can lead to increased memory usage and potential performance issues if not managed properly.\n2. Complexity: Closures can introduce complexity to the code, making it harder to understand and maintain, especially for developers who are not familiar with the concept.\n\nCreating a closure using an anonymous function:\n\n```javascript\nfunction createCounter() {\n  let count = 0;\n  return function() {\n    count++;\n    return count;\n  };\n}\n\nconst counter = createCounter();\nconsole.log(counter()); // 1\nconsole.log(counter()); // 2\n```\n\nCreating a closure using a named function:\n\n```javascript\nfunction createCounter() {\n  let count = 0;\n  function incrementCounter() {\n    count++;\n    return count;\n  }\n  return incrementCounter;\n}\n\nconst counter = createCounter();\nconsole.log(counter()); // 1\nconsole.log(counter()); // 2\n```\n\nImpact of closures on memory management and potential performance implications:\nClosures can have an impact on memory management because they retain access to their outer function's scope even after the outer function has completed its execution. This can lead to increased memory usage, especially if the closure is holding onto large data structures or if there are many instances of the closure.\n\nIn terms of performance, closures can introduce overhead due to the additional memory consumption and the potential for increased garbage collection. However, modern JavaScript engines have become quite efficient at handling closures, so the performance impact is generally minimal in most cases. It is essential to be aware of the potential memory and performance implications when using closures and to manage them appropriately to avoid issues."], "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/3bd3575487f03d857295.json

@@ -0,0 +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": ["- Photosynthesis is a process that converts carbon dioxide into organic compounds, especially sugars, using the energy from sunlight. It primarily occurs in plants, algae, and some bacteria.\n\n1. Light-dependent reactions:\n   - The first stage of photosynthesis begins with the absorption of light by chlorophyll (a pigment found within the chloroplasts of plant cells).\n   - This absorbed light energy splits water molecules into oxygen, hydrogen ions, and electrons.\n   - Oxygen is released as a waste product while the hydrogen ions and electrons are used to generate ATP (adenosine triphosphate) and NADPH (nicotinamide adenine dinucleotide phosphate), which store energy for use in the next stage of photosynthesis.\n\n2. Calvin Cycle or Light-independent reactions:\n   - In this second stage, the ATP and NADPH produced in the light-dependent reactions power the conversion of carbon dioxide into glucose, a type of sugar.\n   - This process involves several steps: carbon fixation, reduction, and regeneration of the starting molecule.\n   - The resulting glucose can be used immediately for energy or stored for later use.\n\nOverall Purpose:\n\n- Photosynthesis plays an essential role in life on Earth because it provides food for plants and oxygen for animals.\n- Plants use the glucose produced during photosynthesis for growth and reproduction, while animals depend on plants for their own nutritional needs.\n- Furthermore, the oxygen generated during photosynthesis is crucial for animal respiration; without it, most forms of life on Earth would not survive.\n- Photosynthesis also plays a significant role in reducing the amount of carbon dioxide in the atmosphere, helping to mitigate climate change.\n- Lastly, fossil fuels, such as coal and oil, originate from ancient organisms that utilized photosynthesis, indicating its long-term impact on Earth's energy resources."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Break down the process of photosynthesis into a bullet-pointed list, detailing each stage and the overall purpose of the process in sustaining life on Earth."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Break down the process of photosynthesis into a bullet-pointed list, detailing each stage and the overall purpose of the process in sustaining life on Earth."], "rejected": ["- Photosynthesis is a process that converts carbon dioxide into organic compounds, especially sugars, using the energy from sunlight. It primarily occurs in plants, algae, and some bacteria.\n\n1. Light-dependent reactions:\n   - The first stage of photosynthesis begins with the absorption of light by chlorophyll (a pigment found within the chloroplasts of plant cells).\n   - This absorbed light energy splits water molecules into oxygen, hydrogen ions, and electrons.\n   - Oxygen is released as a waste product while the hydrogen ions and electrons are used to generate ATP (adenosine triphosphate) and NADPH (nicotinamide adenine dinucleotide phosphate), which store energy for use in the next stage of photosynthesis.\n\n2. Calvin Cycle or Light-independent reactions:\n   - In this second stage, the ATP and NADPH produced in the light-dependent reactions power the conversion of carbon dioxide into glucose, a type of sugar.\n   - This process involves several steps: carbon fixation, reduction, and regeneration of the starting molecule.\n   - The resulting glucose can be used immediately for energy or stored for later use.\n\nOverall Purpose:\n\n- Photosynthesis plays an essential role in life on Earth because it provides food for plants and oxygen for animals.\n- Plants use the glucose produced during photosynthesis for growth and reproduction, while animals depend on plants for their own nutritional needs.\n- Furthermore, the oxygen generated during photosynthesis is crucial for animal respiration; without it, most forms of life on Earth would not survive.\n- Photosynthesis also plays a significant role in reducing the amount of carbon dioxide in the atmosphere, helping to mitigate climate change.\n- Lastly, fossil fuels, such as coal and oil, originate from ancient organisms that utilized photosynthesis, indicating its long-term impact on Earth's energy resources."], "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/3dc9a67845019d35cce1.json

@@ -0,0 +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": ["510814062 / 3 = 170271354"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Find the solution for 510814062 divided by 3. Only mathematical symbols, no verbal descriptions."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Find the solution for 510814062 divided by 3. Only mathematical symbols, no verbal descriptions."], "rejected": ["170271354 stdiv 3"], "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/40c2445260711fc848da.json

@@ -0,0 +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 students are present in the class on that day, we need to follow these steps:\n\n1. Calculate the number of absent students: To do this, we need to multiply the total number of students (25) by the absence rate (20%).\n2. Subtract the number of absent students from the total number of students to find the number of present students.\n\nStep 1: Calculate the number of absent students\n25 students * 20% absence rate = 25 * 0.20 = 5 students are absent\n\nStep 2: Subtract the number of absent students from the total number of students\n25 students - 5 absent students = 20 students are present\n\nSo, there are 20 students present in the class on that day."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["In a class of 25 students, there is a varying attendance rate due to various factors such as illness, appointments, or personal reasons. On a particular day, there is a 20% absence rate. How many students are present in the class on that day?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["In a class of 25 students, there is a varying attendance rate due to various factors such as illness, appointments, or personal reasons. On a particular day, there is a 20% absence rate. How many students are present in the class on that day?"], "rejected": ["To find out how many students are present in the class on that day, we need to follow these steps:\n\n1. Calculate the number of absent students: To do this, we need to multiply the total number of students (25) by the absence rate (20%).\n2. Subtract the number of absent students from the total number of students to find the number of present students.\n\nStep 1: Calculate the number of absent students\n25 students * 20% absence rate = 25 * 0.20 = 5 students are absent\n\nStep 2: Subtract the number of absent students from the total number of students\n25 students - 5 absent students = 20 students are present\n\nSo, there are 20 students present in the class on that day."], "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/463d47e1942343fb7c9a.json

@@ -0,0 +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": ["```cpp\n#include <iostream>\n\nint binarySearch(int* arr, int size, int target) {\n    int* left = arr;\n    int* right = arr + size - 1;\n\n    while (left <= right) {\n        int* mid = left + (right - left) / 2;\n\n        if (*mid == target) {\n            return mid - arr; // Return the index of the found element\n        }\n\n        if (*mid < target) {\n            left = mid + 1;\n        } else {\n            right = mid - 1;\n        }\n    }\n\n    return -1; // Element not found\n}\n\nint main() {\n    int arr[] = {1, 3, 5, 7, 9, 11, 13};\n    int size = sizeof(arr) / sizeof(arr[0]);\n    int target = 11;\n\n    int result = binarySearch(arr, size, target);\n\n    if (result != -1) {\n        std::cout << \"Element found at index: \" << result << std::endl;\n    } else {\n        std::cout << \"Element not found\" << std::endl;\n    }\n\n    return 0;\n}\n```\n\nThis C++ code implements a binary search algorithm using pointers. The `binarySearch` function takes an integer array, its size, and the target value to be searched as input arguments. It returns the index of the target value in the array if it is found, otherwise, it returns -1.\n\nIn the `main` function, we define an example sorted array and call the `binarySearch` function with this array and a target value. The output will display the index of the target value in the array or indicate that the element was not found."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Implement a binary search algorithm in C++ using pointers."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Implement a binary search algorithm in C++ using pointers."], "rejected": ["```cpp\n#include <iostream>\n\nint binarySearch(int* arr, int size, int target) {\n    int* left = arr;\n    int* right = arr + size - 1;\n\n    while (left <= right) {\n        int* mid = left + (right - left) / 2;\n\n        if (*mid == target) {\n            return mid - arr; // Return the index of the found element\n        }\n\n        if (*mid < target) {\n            left = mid + 1;\n        } else {\n            right = mid - 1;\n        }\n    }\n\n    return -1; // Element not found\n}\n\nint main() {\n    int arr[] = {1, 3, 5, 7, 9, 11, 13};\n    int size = sizeof(arr) / sizeof(arr[0]);\n    int target = 11;\n\n    int result = binarySearch(arr, size, target);\n\n    if (result != -1) {\n        std::cout << \"Element found at index: \" << result << std::endl;\n    } else {\n        std::cout << \"Element not found\" << std::endl;\n    }\n\n    return 0;\n}\n```\n\nThis C++ code implements a binary search algorithm using pointers. The `binarySearch` function takes an integer array, its size, and the target value to be searched as input arguments. It returns the index of the target value in the array if it is found, otherwise, it returns -1.\n\nIn the `main` function, we define an example sorted array and call the `binarySearch` function with this array and a target value. The output will display the index of the target value in the array or indicate that the element was not found."], "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/56262c862223bda0d589.json

@@ -0,0 +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 calculate the total distance covered by the train, we can use the formula:\n\nDistance = Speed \u00d7 Time\n\nIn this case, the train maintains a constant speed of 60 miles per hour for a continuous period of 3 hours. So, we can plug these values into the formula:\n\nDistance = 60 miles/hour \u00d7 3 hours\n\nNow, we can perform the multiplication:\n\nDistance = 180 miles\n\nSo, the total distance covered by the train throughout its entire journey is 180 miles."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["A train embarks on a journey maintaining a constant speed of 60 miles per hour for a continuous period of 3 hours, passing through varying terrains and weather conditions. Calculate the total distance covered by the train throughout its entire journey."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["A train embarks on a journey maintaining a constant speed of 60 miles per hour for a continuous period of 3 hours, passing through varying terrains and weather conditions. Calculate the total distance covered by the train throughout its entire journey."], "rejected": ["To calculate the total distance covered by the train, we can use the formula:\n\nDistance = Speed \u00d7 Time\n\nIn this case, the train maintains a constant speed of 60 miles per hour for a continuous period of 3 hours. So, we can plug these values into the formula:\n\nDistance = 60 miles/hour \u00d7 3 hours\n\nNow, we can perform the multiplication:\n\nDistance = 180 miles\n\nSo, the total distance covered by the train throughout its entire journey is 180 miles."], "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/615e0ecaf70f6657a0ea.json

@@ -0,0 +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 overcome feelings of loneliness, begin by acknowledging and validating your emotions. Next, consider engaging in activities that align with your interests, which can help foster connections with like-minded people. Engage in hobbies, join clubs or groups, and attend social events. Focus on developing and maintaining relationships by reaching out to friends or family and communicating openly. Emphasize self-care by engaging in regular exercise, eating well, and getting sufficient sleep. Stay open to professional assistance, such as therapy or counseling, if necessary."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Describe a healthy approach to overcoming feelings of loneliness."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Describe a healthy approach to overcoming feelings of loneliness."], "rejected": ["To overcome feelings of loneliness, begin by acknowledging and validating your emotions. Next, consider engaging in activities that align with your interests, which can help foster connections with like-minded people. Engage in hobbies, join clubs or groups, and attend social events. Focus on developing and maintaining relationships by reaching out to friends or family and communicating openly. Emphasize self-care by engaging in regular exercise, eating well, and getting sufficient sleep. Stay open to professional assistance, such as therapy or counseling, if necessary."], "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/64076dbdb1ee499df01c.json

@@ -0,0 +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 determine the most cost-effective way to allocate funds among these priorities, we need to consider the costs and benefits of each component (replacing old pipes, installing new treatment facilities, and expanding storage capacity) as well as their impact on water supply and quality.\n\n1. Replacing old pipes: Old and deteriorating pipes can lead to leaks, water loss, and contamination issues. By replacing them, the city can reduce water waste, improve water quality, and prevent potential health hazards. The American Water Works Association estimates that the cost of replacing a mile of pipe ranges from $500,000 to $2.5 million, depending on factors such as pipe material, diameter, and location. Assuming an average cost of $1.5 million per mile, the city could replace approximately 667 miles of pipe with its $1 billion budget.\n\n2. Installing new treatment facilities: New treatment facilities will help ensure adequate water quality by removing contaminants and treating wastewater before it is returned to the environment or reused. According to the US Environmental Protection Agency, the average cost of constructing a new drinking water treatment facility ranges from $2 to $4 per gallon of daily capacity. For a city with a population of 1 million people, assuming a daily water consumption rate of 100 gallons per person, the total daily capacity needed would be 100 million gallons. At an average cost of $3 per gallon, building a new treatment facility with this capacity would cost $300 million.\n\n3. Expanding storage capacity: Increasing storage capacity allows the city to store more water during periods of high precipitation and release it during times of drought, ensuring a consistent water supply. The cost of constructing new storage reservoirs varies greatly depending on factors such as size, location, and materials used. However, a rough estimate for a large-scale project might be around $200 million.\n\nBased on these estimates, I propose the following allocation of the $1 billion budget:\n\n- $600 million for replacing old pipes (40% of the budget): This would allow the city to replace approximately 400 miles of pipe, addressing the most critical areas and significantly reducing water loss and contamination risks.\n- $300 million for installing new treatment facilities (30% of the budget): Constructing a new drinking water treatment facility with a capacity of 100 million gallons per day will ensure adequate water quality for the growing population.\n- $100 million for expanding storage capacity (10% of the budget): While this may not be enough to construct a large-scale reservoir, it can still contribute to smaller-scale projects that increase overall storage capacity. The remaining funds could also be used for improving existing infrastructure or investing in alternative water sources such as desalination plants.\n\nThis allocation prioritizes the replacement of old pipes and installation of new treatment facilities, which have direct impacts on water supply and quality. Expanding storage capacity is important but should be considered alongside other strategies for ensuring a consistent water supply, such as promoting water conservation and exploring alternative water sources. By allocating funds in this manner, the city's water department can effectively address its aging infrastructure while meeting the needs of its growing population over the next 20 years."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["A city's water department needs to upgrade its aging infrastructure to meet the growing demands of its population. The project involves replacing old pipes, installing new treatment facilities, and expanding storage capacity. With a budget of $1 billion, determine the most cost-effective way to allocate funds among these priorities while ensuring adequate water supply and quality for the next 20 years. Explain your reasoning.\n\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["A city's water department needs to upgrade its aging infrastructure to meet the growing demands of its population. The project involves replacing old pipes, installing new treatment facilities, and expanding storage capacity. With a budget of $1 billion, determine the most cost-effective way to allocate funds among these priorities while ensuring adequate water supply and quality for the next 20 years. Explain your reasoning.\n\n"], "rejected": ["To determine the most cost-effective way to allocate funds among these priorities, we need to consider the costs and benefits of each component (replacing old pipes, installing new treatment facilities, and expanding storage capacity) as well as their impact on water supply and quality.\n\n1. Replacing old pipes: Old and deteriorating pipes can lead to leaks, water loss, and contamination issues. By replacing them, the city can reduce water waste, improve water quality, and prevent potential health hazards. The American Water Works Association estimates that the cost of replacing a mile of pipe ranges from $500,000 to $2.5 million, depending on factors such as pipe material, diameter, and location. Assuming an average cost of $1.5 million per mile, the city could replace approximately 667 miles of pipe with its $1 billion budget.\n\n2. Installing new treatment facilities: New treatment facilities will help ensure adequate water quality by removing contaminants and treating wastewater before it is returned to the environment or reused. According to the US Environmental Protection Agency, the average cost of constructing a new drinking water treatment facility ranges from $2 to $4 per gallon of daily capacity. For a city with a population of 1 million people, assuming a daily water consumption rate of 100 gallons per person, the total daily capacity needed would be 100 million gallons. At an average cost of $3 per gallon, building a new treatment facility with this capacity would cost $300 million.\n\n3. Expanding storage capacity: Increasing storage capacity allows the city to store more water during periods of high precipitation and release it during times of drought, ensuring a consistent water supply. The cost of constructing new storage reservoirs varies greatly depending on factors such as size, location, and materials used. However, a rough estimate for a large-scale project might be around $200 million.\n\nBased on these estimates, I propose the following allocation of the $1 billion budget:\n\n- $600 million for replacing old pipes (40% of the budget): This would allow the city to replace approximately 400 miles of pipe, addressing the most critical areas and significantly reducing water loss and contamination risks.\n- $300 million for installing new treatment facilities (30% of the budget): Constructing a new drinking water treatment facility with a capacity of 100 million gallons per day will ensure adequate water quality for the growing population.\n- $100 million for expanding storage capacity (10% of the budget): While this may not be enough to construct a large-scale reservoir, it can still contribute to smaller-scale projects that increase overall storage capacity. The remaining funds could also be used for improving existing infrastructure or investing in alternative water sources such as desalination plants.\n\nThis allocation prioritizes the replacement of old pipes and installation of new treatment facilities, which have direct impacts on water supply and quality. Expanding storage capacity is important but should be considered alongside other strategies for ensuring a consistent water supply, such as promoting water conservation and exploring alternative water sources. By allocating funds in this manner, the city's water department can effectively address its aging infrastructure while meeting the needs of its growing population over the next 20 years."], "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/668131cef1041585d5e2.json

@@ -0,0 +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 Beatles\n\n"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["What legendary rock band has members named John, Paul, George, and Ringo?\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["What legendary rock band has members named John, Paul, George, and Ringo?\n"], "rejected": ["The Beatles\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/6dfdb5c3308cc6844def.json

@@ -0,0 +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 film was excessively protracted, resulting in feelings of ennui."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Given the sentence, suggest an improvement using stronger vocabulary and maintaining the meaning.\nThe movie was very long and it made me feel bored."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Given the sentence, suggest an improvement using stronger vocabulary and maintaining the meaning.\nThe movie was very long and it made me feel bored."], "rejected": ["The film was excessively protracted, resulting in feelings of ennui."], "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/709e3aed626cc664ce71.json

@@ -0,0 +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 solve this problem, we need to understand the idea of work and rates. \n\nIn this case, we know that 5 men can dig a hole in 10 hours. That means the rate of work for these 5 men is one hole per 10 hours, or 1/10 of a hole per hour. If we divide this work rate by the number of men, we can determine the work rate per man: 1/10 divided by 5 equals 1/50 of a hole per hour per man.\n\nHowever, we\u2019re told that 3 men work 20% slower than the others. So, these 3 men have a work rate of 80% of 1/50 per hour, or 0.8/50, which equals 0.016 of a hole per hour per man. \n\nThe other 7 men (assuming we have 10 men) work at the original rate of 1/50 of a hole per hour, or 0.02 of a hole per hour per man.\n\nNow, to calculate the total time, we need to add up all the work done per hour and then divide the total work (1 hole) by this sum.\n\nThe total work done per hour by the 3 slower men is 3 * 0.016 = 0.048 of a hole. The total work done per hour by the 7 faster men is 7 * 0.02 = 0.14 of a hole. The total work done per hour by all 10 men is 0.048 + 0.14 = 0.188 of a hole.\n\nTo find out how long it would take these 10 men to dig the hole, we divide 1 (the total work) by 0.188. This gives us approximately 5.31 hours.\n\nSo it would take approximately 5.31 hours for 10 men to dig the same hole, given the conditions about their varying work rates."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["It takes 5 men 10 hours to dig a hole, working at a constant rate. If the work rate of each individual varies according to a logarithmic scale with a base of 2 and 3 men dig 20% slower than the rest, how long would it take 10 men to dig the same hole?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["It takes 5 men 10 hours to dig a hole, working at a constant rate. If the work rate of each individual varies according to a logarithmic scale with a base of 2 and 3 men dig 20% slower than the rest, how long would it take 10 men to dig the same hole?"], "rejected": ["To solve this problem, we need to understand the idea of work and rates. \n\nIn this case, we know that 5 men can dig a hole in 10 hours. That means the rate of work for these 5 men is one hole per 10 hours, or 1/10 of a hole per hour. If we divide this work rate by the number of men, we can determine the work rate per man: 1/10 divided by 5 equals 1/50 of a hole per hour per man.\n\nHowever, we\u2019re told that 3 men work 20% slower than the others. So, these 3 men have a work rate of 80% of 1/50 per hour, or 0.8/50, which equals 0.016 of a hole per hour per man. \n\nThe other 7 men (assuming we have 10 men) work at the original rate of 1/50 of a hole per hour, or 0.02 of a hole per hour per man.\n\nNow, to calculate the total time, we need to add up all the work done per hour and then divide the total work (1 hole) by this sum.\n\nThe total work done per hour by the 3 slower men is 3 * 0.016 = 0.048 of a hole. The total work done per hour by the 7 faster men is 7 * 0.02 = 0.14 of a hole. The total work done per hour by all 10 men is 0.048 + 0.14 = 0.188 of a hole.\n\nTo find out how long it would take these 10 men to dig the hole, we divide 1 (the total work) by 0.188. This gives us approximately 5.31 hours.\n\nSo it would take approximately 5.31 hours for 10 men to dig the same hole, given the conditions about their varying work rates."], "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/7784b06c589d4e970e21.json

@@ -0,0 +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. Interactive Gut Health Quiz: Create an engaging online quiz that assesses users' gut health knowledge and provides personalized recommendations based on their answers. Encourage users to sign up for the waitlist to receive tailored advice and early access to the service.\n2. Social Media Challenges: Launch a series of gut-friendly recipe challenges on social media platforms like Instagram, TikTok, or Facebook. Participants can share their creations using a branded hashtag and sign up for the waitlist to access more recipes and tips.\n3. Influencer Partnerships: Collaborate with health and wellness influencers to share their experiences of improving gut health. They can encourage their followers to join the waitlist for exclusive content and early access to the service.\n4. Webinar Series: Host a series of free webinars featuring experts discussing gut health topics, such as the benefits of probiotics, managing IBS, and understanding the gut-brain connection. Attendees can sign up for the waitlist to access more resources and secure a spot in the upcoming service.\n5. Gut Health Testimonials: Share real-life success stories of people who have improved their gut health, emphasizing the positive impact on their overall well-being. Encourage readers to sign up for the waitlist to receive similar support and guidance.\n6. Early Bird Discounts: Offer limited-time discounts or exclusive bonuses for users who sign up for the waitlist. Promote this offer through email marketing, social media, and online advertising to generate buzz and incentivize sign-ups.\n7. Gamified Referral Program: Implement a referral program that rewards users for inviting friends to join the waitlist. The more friends they refer, the higher they rank on a leaderboard, unlocking special rewards and early access to the service.\n8. Educational Content: Publish a series of informative blog posts, infographics, or videos on gut health topics. Encourage users to sign up for the waitlist to receive more in-depth information, guides, and expert advice.\n9. Virtual Gut Health Summit: Organize a free online event featuring expert speakers, panel discussions, and interactive workshops on gut health. Promote the event and encourage attendees to sign up for the waitlist to continue learning and get priority access to the service.\n10. Limited-Time Access Passes: Announce a contest where a select number of winners will receive early access to the service. Encourage users to sign up for the waitlist and share the contest on social media for a chance to win."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Creative ideas to get users to signup to a waitlist for a service that helps them improve their gut health"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Creative ideas to get users to signup to a waitlist for a service that helps them improve their gut health"], "rejected": ["1. Interactive Gut Health Quiz: Create an engaging online quiz that assesses users' gut health knowledge and provides personalized recommendations based on their answers. Encourage users to sign up for the waitlist to receive tailored advice and early access to the service.\n2. Social Media Challenges: Launch a series of gut-friendly recipe challenges on social media platforms like Instagram, TikTok, or Facebook. Participants can share their creations using a branded hashtag and sign up for the waitlist to access more recipes and tips.\n3. Influencer Partnerships: Collaborate with health and wellness influencers to share their experiences of improving gut health. They can encourage their followers to join the waitlist for exclusive content and early access to the service.\n4. Webinar Series: Host a series of free webinars featuring experts discussing gut health topics, such as the benefits of probiotics, managing IBS, and understanding the gut-brain connection. Attendees can sign up for the waitlist to access more resources and secure a spot in the upcoming service.\n5. Gut Health Testimonials: Share real-life success stories of people who have improved their gut health, emphasizing the positive impact on their overall well-being. Encourage readers to sign up for the waitlist to receive similar support and guidance.\n6. Early Bird Discounts: Offer limited-time discounts or exclusive bonuses for users who sign up for the waitlist. Promote this offer through email marketing, social media, and online advertising to generate buzz and incentivize sign-ups.\n7. Gamified Referral Program: Implement a referral program that rewards users for inviting friends to join the waitlist. The more friends they refer, the higher they rank on a leaderboard, unlocking special rewards and early access to the service.\n8. Educational Content: Publish a series of informative blog posts, infographics, or videos on gut health topics. Encourage users to sign up for the waitlist to receive more in-depth information, guides, and expert advice.\n9. Virtual Gut Health Summit: Organize a free online event featuring expert speakers, panel discussions, and interactive workshops on gut health. Promote the event and encourage attendees to sign up for the waitlist to continue learning and get priority access to the service.\n10. Limited-Time Access Passes: Announce a contest where a select number of winners will receive early access to the service. Encourage users to sign up for the waitlist and share the contest on social media for a chance to win."], "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/8129ae14783b2d388ee8.json

@@ -0,0 +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": ["Solution 1:\nDivide the coins into three groups: two groups of three and one group of two. The first weighing would be between the two groups of three. If they balance, then the heavier coin is in the group of two, and a second weighing of these two will reveal the heavier coin. If the two groups of three do not balance, take the heavier group and divide it into three individual coins. Weighing any two of these will either identify the heavier coin or indicate that the unweighed coin is the heaviest.\n\nFinal answer: In this scenario, you can find the heavier coin with at most 2 weighings.\n\nSolution 2:\nAnother method involves dividing the coins into two groups of four for the first weighing. If they balance, the heavier coin is the one left out, but if they don't, we know the heavier coin is in the heavier group. For the second weighing, divide the heavier group into two sets of two. If they balance, the heavier coin is one of the remaining two, and a third weighing will reveal which one it is. If they don't balance, the heavier set contains the heavier coin. A final weighing of those two coins will determine which one is heavier.\n\nFinal answer: This approach requires at most 3 weighings to find the heavier coin.\n\nSolution 3:\nA less efficient strategy would involve weighing each coin individually against all others. Start by weighing the first coin against the second. If they balance, move on to the third coin, and so forth until you find the heavier coin. \n\nFinal answer: This method could require up to 7 weighings to find the heavier coin.\n\nRanking of solutions from best to worst:\n1. Solution 1: This solution is the most efficient as it uses the fewest number of weighings.\n2. Solution 2: Although this solution also works, it potentially requires more weighings than solution 1.\n3. Solution 3: This solution is the least efficient because it may require the maximum possible number of weighings.\n\nBest and final answer: You can find the heavier coin with at most 2 weighings (Solution 1)."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["You have 8 identical coins and one which is slightly heavier. Using a balance scale, what's the minimum number of weighings needed to find the heavier coin? Present multiple solutions, explaining your steps thoroughly. Afterward, arrange these solutions from most to least accurate, and select the best answer."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["You have 8 identical coins and one which is slightly heavier. Using a balance scale, what's the minimum number of weighings needed to find the heavier coin? Present multiple solutions, explaining your steps thoroughly. Afterward, arrange these solutions from most to least accurate, and select the best answer."], "rejected": ["Solution 1:\nDivide the coins into three groups: two groups of three and one group of two. The first weighing would be between the two groups of three. If they balance, then the heavier coin is in the group of two, and a second weighing of these two will reveal the heavier coin. If the two groups of three do not balance, take the heavier group and divide it into three individual coins. Weighing any two of these will either identify the heavier coin or indicate that the unweighed coin is the heaviest.\n\nFinal answer: In this scenario, you can find the heavier coin with at most 2 weighings.\n\nSolution 2:\nAnother method involves dividing the coins into two groups of four for the first weighing. If they balance, the heavier coin is the one left out, but if they don't, we know the heavier coin is in the heavier group. For the second weighing, divide the heavier group into two sets of two. If they balance, the heavier coin is one of the remaining two, and a third weighing will reveal which one it is. If they don't balance, the heavier set contains the heavier coin. A final weighing of those two coins will determine which one is heavier.\n\nFinal answer: This approach requires at most 3 weighings to find the heavier coin.\n\nSolution 3:\nA less efficient strategy would involve weighing each coin individually against all others. Start by weighing the first coin against the second. If they balance, move on to the third coin, and so forth until you find the heavier coin. \n\nFinal answer: This method could require up to 7 weighings to find the heavier coin.\n\nRanking of solutions from best to worst:\n1. Solution 1: This solution is the most efficient as it uses the fewest number of weighings.\n2. Solution 2: Although this solution also works, it potentially requires more weighings than solution 1.\n3. Solution 3: This solution is the least efficient because it may require the maximum possible number of weighings.\n\nBest and final answer: You can find the heavier coin with at most 2 weighings (Solution 1)."], "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/8694bae1a986c00ced62.json

@@ -0,0 +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": ["Since I am an adversary trying to decrypt messages sent to Bob using RSA, and I correctly guess that gcd(78373, 168437) \u2260 1, I will first find the greatest common divisor (gcd) of the two numbers.\n\nCan you calculate the following expression for me: gcd(78373, 168437)?"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["You are an adversary trying to decrypt messages sent to Bob using RSA. Bob\u2019s public key is (7, 168437). Bob is sent the ciphertext 78373. You correctly guess that gcd(78373, 168437) \u2260 1. What is Bob\u2019s private key?\n\nSolve the problem step by step, if you need to do a heavy calculation, ask me, for examples, say, \"Can you calculate the following expressions/values for me: ...?\""], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["You are an adversary trying to decrypt messages sent to Bob using RSA. Bob\u2019s public key is (7, 168437). Bob is sent the ciphertext 78373. You correctly guess that gcd(78373, 168437) \u2260 1. What is Bob\u2019s private key?\n\nSolve the problem step by step, if you need to do a heavy calculation, ask me, for examples, say, \"Can you calculate the following expressions/values for me: ...?\""], "rejected": ["Since I am an adversary trying to decrypt messages sent to Bob using RSA, and I correctly guess that gcd(78373, 168437) \u2260 1, I will first find the greatest common divisor (gcd) of the two numbers.\n\nCan you calculate the following expression for me: gcd(78373, 168437)?"], "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/91e46f843788bea11dba.json

@@ -0,0 +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 trigger both `customScript` and `pageHeaderFooter` scripts on page load, you need to update the `onLoad` option in the `Options` object as follows:\n\n```javascript\nonLoad: () => {\n  customScript(App);\n  pageHeaderFooter(App);\n},\n```\n\nThis will ensure that both scripts are executed when the page is loaded using the ENGrid framework. `customScript` will be called first, followed by `pageHeaderFooter`.\n\nFinally, don't forget to import both scripts, like this:\n\n```javascript\nimport \"./sass/main.scss\";\nimport { customScript, pageHeaderFooter } from \"./scripts/main\";\n\nconst options: Options = {\n  // Options goes here\n  // ...\n  onLoad: () => {\n    customScript(App);\n    pageHeaderFooter(App);\n  },\n};\n\nnew App(options);\n```\n\nNow, both scripts will be triggered on page load seamlessly through the ENGrid framework."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["For this code, how can in trigger pageHeaderFooter in addition to customScript?\nimport { Options, App, ApplePay } from \"@4site/engrid-common\"; // Uses ENGrid via NPM\n// import { Options, App } from \"../../engrid-scripts/packages/common\"; // Uses ENGrid via Visual Studio Workspace\n\nimport \"./sass/main.scss\";\nimport { customScript } from \"./scripts/main\";\nimport { pageHeaderFooter } from \"./scripts/page-header-footer\";\n\nconst options: Options = {\n applePay: false,\n CapitalizeFields: true,\n ClickToExpand: true,\n CurrencySymbol: \"$\",\n DecimalSeparator: \".\",\n ThousandsSeparator: \",\",\n MediaAttribution: true,\n SkipToMainContentLink: true,\n SrcDefer: true,\n ProgressBar: true,\n Debug: App.getUrlParameter(\"debug\") == \"true\" ? true : false,\n onLoad: () => customScript(App),\n onResize: () => console.log(\"Starter Theme Window Resized\"),\n};\nnew App(options);"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["For this code, how can in trigger pageHeaderFooter in addition to customScript?\nimport { Options, App, ApplePay } from \"@4site/engrid-common\"; // Uses ENGrid via NPM\n// import { Options, App } from \"../../engrid-scripts/packages/common\"; // Uses ENGrid via Visual Studio Workspace\n\nimport \"./sass/main.scss\";\nimport { customScript } from \"./scripts/main\";\nimport { pageHeaderFooter } from \"./scripts/page-header-footer\";\n\nconst options: Options = {\n applePay: false,\n CapitalizeFields: true,\n ClickToExpand: true,\n CurrencySymbol: \"$\",\n DecimalSeparator: \".\",\n ThousandsSeparator: \",\",\n MediaAttribution: true,\n SkipToMainContentLink: true,\n SrcDefer: true,\n ProgressBar: true,\n Debug: App.getUrlParameter(\"debug\") == \"true\" ? true : false,\n onLoad: () => customScript(App),\n onResize: () => console.log(\"Starter Theme Window Resized\"),\n};\nnew App(options);"], "rejected": [" To include the triggering of `pageHeaderFooter` along with `customScript`, you need to modify the `options` object to assign the given function to both the `onLoad` and `onInitialize` parameters (as `onLoad` is only associated with `customScript` in your current code). `onInitialize` will ensure that `pageHeaderFooter` is initialized before the rest of the application. Here's how you can update the code:\n\n```javascript\nimport { Options, App, ApplePay } from \"@4site/engrid-common\"; // Uses ENGrid via NPM\n// import { Options, App } from \"../../engrid-scripts/packages/common\"; // Uses ENGrid via Visual Studio Workspace\n\nimport \"./sass/main.scss\";\nimport { customScript } from \"./scripts/main\";\nimport { pageHeaderFooter } from \"./scripts/page-header-footer\";\n\nconst options: Options = {\n  applePay: false,\n  CapitalizeFields: true,\n  ClickToExpand: true,\n  CurrencySymbol: \"$\",\n  DecimalSeparator: \".\",\n  ThousandsSeparator: \",\",\n  MediaAttribution: true,\n  SkipToMainContentLink: true,\n  SrcDefer: true,\n  ProgressBar: true,\n  Debug: App.getUrlParameter(\"debug\") === \"true\" ? true : false,\n  onLoad: () => customScript(App), // We keep the existing onLoad for customScript\n  onInitialize: () => pageHeaderFooter(App), // Add a new parameter for pageHeaderFooter\n  onResize: () => console.log(\"Starter Theme Window Resized\"),\n};\n\nnew App(options);\n```\n\nBy adding the `onInitialize` callback function for the `pageHeaderFooter`, the custom script (`customScript`) and the `pageHeaderFooter` will both be executed when the application initializes.\n"], "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/95e3be0e73b6d6da4013.json

@@ -0,0 +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": ["Sherlock Holmes"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["In literature, what character created by Sir Arthur Conan Doyle was known for his deductive reasoning and violin playing?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["In literature, what character created by Sir Arthur Conan Doyle was known for his deductive reasoning and violin playing?"], "rejected": ["Sherlock Holmes"], "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/9c2d6a1514ea1f6b6576.json

@@ -0,0 +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 Maclaurin series expansion of a function f(x) is given by:\n\nf(x) = f(0) + f'(0)x + (1/2!)f''(0)x^2 + (1/3!)f'''(0)x^3 + (1/4!)f''''(0)x^4 + ...\n\nFor sin(x), we have:\nf(x) = sin(x)\nf'(x) = cos(x)\nf''(x) = -sin(x)\nf'''(x) = -cos(x)\nf''''(x) = sin(x)\n\nNow, we need to find the values of these derivatives at x=0:\n\nf(0) = sin(0) = 0\nf'(0) = cos(0) = 1\nf''(0) = -sin(0) = 0\nf'''(0) = -cos(0) = -1\nf''''(0) = sin(0) = 0\n\nSo, the Maclaurin series expansion up to the fifth degree term is:\n\nsin(x) \u2248 0 + 1*x + (1/2!)*0*x^2 + (1/3!)*(-1)*x^3 + (1/4!)*0*x^4\n\nSimplifying this expression, we get:\n\nsin(x) \u2248 x - (1/6)x^3"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Find the Maclaurin series expansion of sin(x) up to the fifth degree term."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Find the Maclaurin series expansion of sin(x) up to the fifth degree term."], "rejected": ["The Maclaurin series expansion of a function f(x) is given by:\n\nf(x) = f(0) + f'(0)x + (1/2!)f''(0)x^2 + (1/3!)f'''(0)x^3 + (1/4!)f''''(0)x^4 + ...\n\nFor sin(x), we have:\nf(x) = sin(x)\nf'(x) = cos(x)\nf''(x) = -sin(x)\nf'''(x) = -cos(x)\nf''''(x) = sin(x)\n\nNow, we need to find the values of these derivatives at x=0:\n\nf(0) = sin(0) = 0\nf'(0) = cos(0) = 1\nf''(0) = -sin(0) = 0\nf'''(0) = -cos(0) = -1\nf''''(0) = sin(0) = 0\n\nSo, the Maclaurin series expansion up to the fifth degree term is:\n\nsin(x) \u2248 0 + 1*x + (1/2!)*0*x^2 + (1/3!)*(-1)*x^3 + (1/4!)*0*x^4\n\nSimplifying this expression, we get:\n\nsin(x) \u2248 x - (1/6)x^3"], "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/a6d3ec29eb2901fccbb1.json

@@ -0,0 +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": ["c) Adam Smith"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Who is considered the father of modern economics?\na) Karl Marx\nb) John Maynard Keynes\nc) Adam Smith\nd) Milton Friedman\n\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Who is considered the father of modern economics?\na) Karl Marx\nb) John Maynard Keynes\nc) Adam Smith\nd) Milton Friedman\n\n"], "rejected": ["c) Adam Smith"], "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/a9421fe4d33e15bda48a.json

@@ -0,0 +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 two marathon runners demonstrated great skill and endurance, pulling ahead of the rest of the competitors. In the final stretch, they remained level with each other, showcasing their admirable parity."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Equal in magnitude, extent, or degree, and an anagram of \"egal\"."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Equal in magnitude, extent, or degree, and an anagram of \"egal\"."], "rejected": ["The two marathon runners demonstrated great skill and endurance, pulling ahead of the rest of the competitors. In the final stretch, they remained level with each other, showcasing their admirable parity."], "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/a94dd4f22b1fc8661f31.json

@@ -0,0 +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": ["One significant scientific term related to climate change is \"Greenhouse Gas Effect.\" This effect is the process by which greenhouse gases, such as water vapour, carbon dioxide, methane, nitrous oxide, and ozone, trap heat from the sun in the Earth's atmosphere, leading to a rise in the planet's temperature. While the natural presence of these gases in the atmosphere helps to make the planet habitable by keeping it warm, their concentrations have been rapidly rising due to human activities, particularly the burning of fossil fuels. As a result, global temperatures are rising at an unprecedented rate, leading to climate change and its associated impacts on ecosystems and human societies."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Find and define a scientific term related to climate change.\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Find and define a scientific term related to climate change.\n"], "rejected": ["One significant scientific term related to climate change is \"Greenhouse Gas Effect.\" This effect is the process by which greenhouse gases, such as water vapour, carbon dioxide, methane, nitrous oxide, and ozone, trap heat from the sun in the Earth's atmosphere, leading to a rise in the planet's temperature. While the natural presence of these gases in the atmosphere helps to make the planet habitable by keeping it warm, their concentrations have been rapidly rising due to human activities, particularly the burning of fossil fuels. As a result, global temperatures are rising at an unprecedented rate, leading to climate change and its associated impacts on ecosystems and human societies."], "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/afab97a0fc1cea1524aa.json

@@ -0,0 +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: Determine the new amounts of flour and sugar.\nThe recipe calls for a ratio of 3 cups of flour to 2 cups of sugar. To increase the total volume of ingredients by a factor of 1.5 while maintaining the same ratio, we need to multiply both the flour and sugar amounts by 1.5.\n\nStep 2: Calculate the new amount of flour.\nNew amount of flour = original amount of flour * 1.5\nNew amount of flour = 3 cups * 1.5\nNew amount of flour = 4.5 cups\n\nStep 3: Calculate the new amount of sugar.\nNew amount of sugar = original amount of sugar * 1.5\nNew amount of sugar = 2 cups * 1.5\nNew amount of sugar = 3 cups\n\nStep 4: Calculate the total number of cups required.\nTotal number of cups = new amount of flour + new amount of sugar\nTotal number of cups = 4.5 cups + 3 cups\nTotal number of cups = 7.5 cups\n\nSo, you would need 4.5 cups of flour and 3 cups of sugar, making a total of 7.5 cups of ingredients."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["A recipe calls for a ratio of 3 cups of flour to 2 cups of sugar. If you need to increase the total volume of ingredients by a factor of 1.5 while maintaining the same ratio, how many cups of each ingredient would be needed and what is the total number of cups required?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["A recipe calls for a ratio of 3 cups of flour to 2 cups of sugar. If you need to increase the total volume of ingredients by a factor of 1.5 while maintaining the same ratio, how many cups of each ingredient would be needed and what is the total number of cups required?"], "rejected": ["Step 1: Determine the new amounts of flour and sugar.\nThe recipe calls for a ratio of 3 cups of flour to 2 cups of sugar. To increase the total volume of ingredients by a factor of 1.5 while maintaining the same ratio, we need to multiply both the flour and sugar amounts by 1.5.\n\nStep 2: Calculate the new amount of flour.\nNew amount of flour = original amount of flour * 1.5\nNew amount of flour = 3 cups * 1.5\nNew amount of flour = 4.5 cups\n\nStep 3: Calculate the new amount of sugar.\nNew amount of sugar = original amount of sugar * 1.5\nNew amount of sugar = 2 cups * 1.5\nNew amount of sugar = 3 cups\n\nStep 4: Calculate the total number of cups required.\nTotal number of cups = new amount of flour + new amount of sugar\nTotal number of cups = 4.5 cups + 3 cups\nTotal number of cups = 7.5 cups\n\nSo, you would need 4.5 cups of flour and 3 cups of sugar, making a total of 7.5 cups of ingredients."], "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/bcd364fcb19adb8aed6e.json

@@ -0,0 +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 answer the question, consider the following: The advantage of task-oriented leadership is that it ensures that deadlines are met and jobs are completed, and it's especially useful for team members who do n't manage their time well.\nTherefore, the answer is it 's especially useful for team members who do n't manage their time well."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Give some stream of consciousness and then the answer. Context: The advantage of task-oriented leadership is that it ensures that deadlines are met and jobs are completed, and it's especially useful for team members who do n't manage their time well. Additionally, these types of leaders tend to exemplify a strong understanding of how to get the job done, focusing on the necessary workplace procedures and delegating work accordingly to ensure that everything gets done in a timely and productive manner.\\n\\nQuestion: when is task focused leader behavior most important?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Give some stream of consciousness and then the answer. Context: The advantage of task-oriented leadership is that it ensures that deadlines are met and jobs are completed, and it's especially useful for team members who do n't manage their time well. Additionally, these types of leaders tend to exemplify a strong understanding of how to get the job done, focusing on the necessary workplace procedures and delegating work accordingly to ensure that everything gets done in a timely and productive manner.\\n\\nQuestion: when is task focused leader behavior most important?"], "rejected": ["To answer the question, consider the following: The advantage of task-oriented leadership is that it ensures that deadlines are met and jobs are completed, and it's especially useful for team members who do n't manage their time well.\nTherefore, the answer is it 's especially useful for team members who do n't manage their time well."], "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/d2796322f991951db0f6.json

@@ -0,0 +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 calculate the de Broglie wavelength of an electron with a given kinetic energy, we can use the de Broglie equation:\n\n\u03bb = h / p\n\nwhere \u03bb represents the de Broglie wavelength, h is the Planck's constant (approximately 6.626 x 10^-34 Js), and p is the electron's momentum.\n\nFirst, we need to find the electron's momentum. We can do this using the relation between kinetic energy and momentum:\n\nKinetic energy (KE) = 0.5 * m * v^2\n\nwhere KE is the kinetic energy, m is the electron's mass (approximately 9.109 x 10^-31 kg), and v is its velocity. We have the kinetic energy in electron volts (eV), which we need to convert into joules (J). To do this, we can use the conversion factor 1 eV = 1.602 x 10^-19 J.\n\nRearranging the equation above to find the velocity, we get:\n\nv = sqrt((2 * KE) / m)\n\nNext, we can use the relation between the momentum (p) and the velocity (v):\n\np = m * v\n\nNow that we have the momentum, we can plug it into the de Broglie equation:\n\n\u03bb = h / p\n\nFinally, we will need to convert the de Broglie wavelength from meters (m) to nanometers (nm). To convert from m to nm, you can use this conversion factor: 1 m = 10^9 nm.\n\nNow let's perform these calculations:\n\n1. Convert kinetic energy to Joules:\n50 eV * (1.602 x 10^-19 J / 1 eV) = 8.01 x 10^-19 J\n\n2. Calculate the electron's velocity:\nv = sqrt((2 * 8.01 x 10^-19 J) / (9.109 x 10^-31 kg)) = 1.591 x 10^6 m/s\n\n3. Calculate the electron's momentum:\np = (9.109 x 10^-31 kg) * (1.591 x 10^6 m/s) = 1.434 x 10^-24 kg\u00b7m/s\n\n4. Calculate the de Broglie wavelength:\n\u03bb = (6.626 x 10^-34 Js) / (1.434 x 10^-24 kg\u00b7m/s) = 4.623 x 10^-10 m\n\n5. Convert the de Broglie wavelength to nanometers:\n4.623 x 10^-10 m * (10^9 nm / 1 m) = 4.623 nm\n\nSo, the de Broglie wavelength of an electron with a kinetic energy of 50 eV is approximately 4.623 nm."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Calculate the de Broglie Wavelength, in nm, of an electron with kinetic energy 50 eV."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Calculate the de Broglie Wavelength, in nm, of an electron with kinetic energy 50 eV."], "rejected": ["Wave Theorem, also known as the Wave Equation, is a fundamental concept in physics that describes the behavior of waves, such as sound waves, light waves, and water waves. It is a partial differential equation that relates the wave's displacement at a given point in space and time to the properties of the medium through which the wave is propagating.\n\nThe general form of the wave equation is:\n\n\u2202\u00b2\u03c8/\u2202t\u00b2 = c\u00b2 \u2207\u00b2\u03c8\n\nHere, \u03c8 represents the wave's displacement, t is time, c is the wave's speed, and \u2207\u00b2 is the Laplacian operator, which represents the spatial derivatives of the wave's displacement. The equation states that the acceleration of the wave's displacement with respect to time (\u2202\u00b2\u03c8/\u2202t\u00b2) is proportional to the spatial curvature of the wave (\u2207\u00b2\u03c8) multiplied by the square of the wave's speed (c\u00b2).\n\nThe wave equation is essential in understanding various phenomena in physics, such as the propagation of sound in air, the behavior of electromagnetic waves, and the motion of waves on a string or in a fluid. It helps predict the behavior of waves under different conditions and is widely used in engineering, acoustics, optics, and other fields.\n Thus the answer is: 0.17"], "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/d519bd0e72d15340a6bf.json

@@ -0,0 +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 code appears to be part of a PHP class for managing tasks. The method actiongetRMTasks() retrieves tasks from a database table called tbl\\_task and returns them in descending order based on the task\\_id. Here's a brief explanation of the code:\n\n1. It checks if a valid admin token is provided. If not, it returns an error message and ends the process.\n2. It initializes an empty array called $tasks.\n3. It prepares a SQL query to select all columns from the tbl\\_task table, ordered by task\\_id in descending order.\n4. It executes the SQL query using the rm object's createCommand() method and queryAll() method, and stores the result in the $tasks array.\n5. It sets the response details with the retrieved tasks as the 'data' key.\n6. It sets the response code to 1, indicating a successful operation.\n7. It calls the output() method to send the response.\n\nThe code seems to be working correctly for its purpose, which is to retrieve tasks from the database and return them. However, I would recommend adding comments to the code to improve readability, and it's also a good practice to use more meaningful variable names. For instance, 'rm' could be renamed to something more descriptive, such as 'dbConnection'."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["check this code to get tasks \n// Rm APIs end\n\n public function actiongetRMTasks() { \n if ( !$token=adminComponent::getAdminByToken($this->data['token'])) {\n $this->msg=self::t(\"Token not valid\");\n $this->output();\n Yii::app()->end();\n } \n\n $tasks = array();\n\n $stmt = \"SELECT \\* FROM tbl\\_task ORDER BY task\\_id DESC\";\n\n $tasks = $this->rm->createCommand($stmt)->queryAll();\n\n $this->details = array(\n 'data' => $tasks,\n ); \n $this->code = 1;\n $this->output();\n\n }"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["check this code to get tasks \n// Rm APIs end\n\n public function actiongetRMTasks() { \n if ( !$token=adminComponent::getAdminByToken($this->data['token'])) {\n $this->msg=self::t(\"Token not valid\");\n $this->output();\n Yii::app()->end();\n } \n\n $tasks = array();\n\n $stmt = \"SELECT \\* FROM tbl\\_task ORDER BY task\\_id DESC\";\n\n $tasks = $this->rm->createCommand($stmt)->queryAll();\n\n $this->details = array(\n 'data' => $tasks,\n ); \n $this->code = 1;\n $this->output();\n\n }"], "rejected": ["This code appears to be part of a PHP class for managing tasks. The method actiongetRMTasks() retrieves tasks from a database table called tbl\\_task and returns them in descending order based on the task\\_id. Here's a brief explanation of the code:\n\n1. It checks if a valid admin token is provided. If not, it returns an error message and ends the process.\n2. It initializes an empty array called $tasks.\n3. It prepares a SQL query to select all columns from the tbl\\_task table, ordered by task\\_id in descending order.\n4. It executes the SQL query using the rm object's createCommand() method and queryAll() method, and stores the result in the $tasks array.\n5. It sets the response details with the retrieved tasks as the 'data' key.\n6. It sets the response code to 1, indicating a successful operation.\n7. It calls the output() method to send the response.\n\nThe code seems to be working correctly for its purpose, which is to retrieve tasks from the database and return them. However, I would recommend adding comments to the code to improve readability, and it's also a good practice to use more meaningful variable names. For instance, 'rm' could be renamed to something more descriptive, such as 'dbConnection'."], "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/e88a0d725272d946feba.json

@@ -0,0 +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 calculate the amount of water consumed in a month, first, we need to convert liters to gallons. We know that:\n\n1 liter \u2248 0.264172 gallons\n\nSo,\n3 liters \u2248 3 * 0.264172 = 0.792516 gallons\n\nNow, assuming 30 days in a month, the total amount of water consumed in a month would be:\n\nTotal water consumption = Daily water consumption \u00d7 Number of days\n                                       = 0.792516 gallons/day \u00d7 30 days\n                                       = 23.77548 gallons\n\nSo, the person drinks approximately 23.78 gallons of water in a month (when rounded to two decimal places)."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["If a person drinks an average of 3 liters of water per day, how many gallons of water do they drink in a month (assuming 30 days)?\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["If a person drinks an average of 3 liters of water per day, how many gallons of water do they drink in a month (assuming 30 days)?\n"], "rejected": ["To calculate the amount of water consumed in a month, first, we need to convert liters to gallons. We know that:\n\n1 liter \u2248 0.264172 gallons\n\nSo,\n3 liters \u2248 3 * 0.264172 = 0.792516 gallons\n\nNow, assuming 30 days in a month, the total amount of water consumed in a month would be:\n\nTotal water consumption = Daily water consumption \u00d7 Number of days\n                                       = 0.792516 gallons/day \u00d7 30 days\n                                       = 23.77548 gallons\n\nSo, the person drinks approximately 23.78 gallons of water in a month (when rounded to two decimal places)."], "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/e921056d62bcad0b4024.json

@@ -0,0 +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": ["One planet from our solar system is Jupiter."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Name a planet from our solar system."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Name a planet from our solar system."], "rejected": ["One planet from our solar system is Jupiter."], "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/ecf2b305b790ec48b367.json

@@ -0,0 +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": ["7818 x 86175 = 674667340"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Please give me the computation for 7818x86175. Only mathematical symbols, no verbal descriptions."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Please give me the computation for 7818x86175. Only mathematical symbols, no verbal descriptions."], "rejected": ["7818 * 86175 = 86175 * (7000 + 800 + 10 + 8) = 86175 * 7000 + 86175 * 800 + 86175 * 10 + 86175 * 8 = 603225000 + 68940000 + 861750 + 689400 = 672165000 + 861750 + 689400 = 673026750 + 689400 = 673716150"], "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/f3080c3a936723d92d25.json

@@ -0,0 +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": ["Headline 1: \"Revolutionize Your Home Comfort: Discover Energy-Saving Heat Pumps Today!\"\nHeadline 2: \"Effortless Climate Control: Unlock the Power of Simply Smart Home's Heat Pumps\"\nHeadline 3: \"Stay Warm, Save Energy: Embrace the Future with Our Innovative Heat Pump Solutions\""], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Act as the best Copywriter, Use creative and Engaging language. Use the best strategies to write content for the Simply Smart Home Website. Here's the tone and brand voice to use for the Content. Write the content based upon the request. \nMain Purpose: Simply Smart Home's main purpose is to provide innovative, sustainable, and reliable HVAC solutions for homeowners and businesses, ensuring comfort, energy efficiency, and peace of mind.\n\nTarget Audience: Simply Smart Home's target audience includes homeowners, property managers, businesses, and environmentally conscious consumers seeking smart, energy-efficient HVAC systems and home services.\n\nDesired Feelings: Simply Smart Home wants its brand to make people feel secure, empowered, and confident in the quality and efficiency of their HVAC systems and services.\n\nCore Values: Simply Smart Home's core values include innovation, customer focus, reliability, sustainability, and professionalism.\n\nBrand Personality: Simply Smart Home's brand personality is innovative, dependable, knowledgeable, eco-friendly, and customer-centric.\n\nBrand Description: Simply Smart Home is a cutting-edge HVAC company that specializes in providing energy-efficient heating, cooling, and home automation solutions, designed to enhance comfort and convenience for homeowners and businesses while reducing their environmental footprint.\n\nAdjectives: Innovative, reliable, sustainable, customer-oriented, knowledgeable, and eco-friendly.\n\nTone of Voice: Simply Smart Home's tone of voice is informative, approachable, and inspiring.\n\nLanguage Style: Simply Smart Home's language style is clear, concise, and engaging, using everyday language to communicate the benefits and features of their smart HVAC solutions and services.\n\nUnique Words and Phrases: \"Simply Efficient,\" \"Smart Comfort Solutions,\" \"Intelligent HVAC Systems,\" \"Eco-friendly Home Automation.\"\n\nSentence Structure and Length: Simply Smart Home's typical sentence structure is simple and direct, using a mix of short and medium-length sentences to convey information efficiently and effectively.\n\nMessaging Examples:\n\"Upgrade to Simply Smart Home's intelligent HVAC solutions for energy-efficient comfort all year round.\"\n\"Experience the future of home automation with our eco-friendly, smart home systems.\"\n\"Stay cozy and save energy with Simply Smart Home's innovative heating and cooling solutions.\"\n\"Take control of your home's comfort and efficiency with our smart, sustainable HVAC systems.\" \nRequest - Write 3 Headlines for Heat Pumps Page\n\nPlease write in English language."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Act as the best Copywriter, Use creative and Engaging language. Use the best strategies to write content for the Simply Smart Home Website. Here's the tone and brand voice to use for the Content. Write the content based upon the request. \nMain Purpose: Simply Smart Home's main purpose is to provide innovative, sustainable, and reliable HVAC solutions for homeowners and businesses, ensuring comfort, energy efficiency, and peace of mind.\n\nTarget Audience: Simply Smart Home's target audience includes homeowners, property managers, businesses, and environmentally conscious consumers seeking smart, energy-efficient HVAC systems and home services.\n\nDesired Feelings: Simply Smart Home wants its brand to make people feel secure, empowered, and confident in the quality and efficiency of their HVAC systems and services.\n\nCore Values: Simply Smart Home's core values include innovation, customer focus, reliability, sustainability, and professionalism.\n\nBrand Personality: Simply Smart Home's brand personality is innovative, dependable, knowledgeable, eco-friendly, and customer-centric.\n\nBrand Description: Simply Smart Home is a cutting-edge HVAC company that specializes in providing energy-efficient heating, cooling, and home automation solutions, designed to enhance comfort and convenience for homeowners and businesses while reducing their environmental footprint.\n\nAdjectives: Innovative, reliable, sustainable, customer-oriented, knowledgeable, and eco-friendly.\n\nTone of Voice: Simply Smart Home's tone of voice is informative, approachable, and inspiring.\n\nLanguage Style: Simply Smart Home's language style is clear, concise, and engaging, using everyday language to communicate the benefits and features of their smart HVAC solutions and services.\n\nUnique Words and Phrases: \"Simply Efficient,\" \"Smart Comfort Solutions,\" \"Intelligent HVAC Systems,\" \"Eco-friendly Home Automation.\"\n\nSentence Structure and Length: Simply Smart Home's typical sentence structure is simple and direct, using a mix of short and medium-length sentences to convey information efficiently and effectively.\n\nMessaging Examples:\n\"Upgrade to Simply Smart Home's intelligent HVAC solutions for energy-efficient comfort all year round.\"\n\"Experience the future of home automation with our eco-friendly, smart home systems.\"\n\"Stay cozy and save energy with Simply Smart Home's innovative heating and cooling solutions.\"\n\"Take control of your home's comfort and efficiency with our smart, sustainable HVAC systems.\" \nRequest - Write 3 Headlines for Heat Pumps Page\n\nPlease write in English language."], "rejected": ["Headline 1: \"Revolutionize Your Home Comfort: Discover Energy-Saving Heat Pumps Today!\"\nHeadline 2: \"Effortless Climate Control: Unlock the Power of Simply Smart Home's Heat Pumps\"\nHeadline 3: \"Stay Warm, Save Energy: Embrace the Future with Our Innovative Heat Pump Solutions\""], "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/f5676cda2f40f8e2347b.json

@@ -0,0 +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": ["Costa Rica\n\n"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Which Central American country has two coastlines, one on the Caribbean Sea and another on the Pacific Ocean?\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Which Central American country has two coastlines, one on the Caribbean Sea and another on the Pacific Ocean?\n"], "rejected": ["Costa Rica\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/f7532be0046e3c5ad833.json

@@ -0,0 +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": ["Ruth"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Identify the subject of the sentence.\nOutput should be a single word.\nRuth and Molly made cookies for the bake sale."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Identify the subject of the sentence.\nOutput should be a single word.\nRuth and Molly made cookies for the bake sale."], "rejected": ["Ruth"], "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/fa5ca960a04ba1e7e252.json

@@ -0,0 +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": ["c) Right to freedom of religion"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Which of the following is a fundamental human right, according to the United Nations Universal Declaration of Human Rights?\na) Right to a private jet\nb) Right to a minimum wage\nc) Right to freedom of religion\nd) Right to a carbon-free environment\n\n"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Which of the following is a fundamental human right, according to the United Nations Universal Declaration of Human Rights?\na) Right to a private jet\nb) Right to a minimum wage\nc) Right to freedom of religion\nd) Right to a carbon-free environment\n\n"], "rejected": ["c) Right to freedom of religion"], "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/fafd887d5bdf76b77650.json

@@ -0,0 +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": ["330287409113743"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Determine the outcome of 112526713355575 + 215689614748625 by performing the calculation. Only respond with math and no words."], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Determine the outcome of 112526713355575 + 215689614748625 by performing the calculation. Only respond with math and no words."], "rejected": [" The result of the calculation 112526713355575 + 215689614748625 is 328216328084199.\");\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}