Synchronizing local compiler cache.

neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B/61dbcee822943df69106.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": ["This statement demonstrates the logical fallacy known as 'hasty generalization.' The speaker draws a faulty conclusion about vegetarians' impact on the environment based on a single, isolated example of a vegetarian who drives a gas-guzzling car. Anecdotal evidence like this cannot be used to make general claims about a larger population."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Identify the logical fallacy in this statement: 'Just because you're a vegetarian doesn't mean you're saving the environment. My friend's cousin is a vegetarian, and she drives a gas-guzzling car.'"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Identify the logical fallacy in this statement: 'Just because you're a vegetarian doesn't mean you're saving the environment. My friend's cousin is a vegetarian, and she drives a gas-guzzling car.'"], "rejected": ["This statement demonstrates the logical fallacy known as 'hasty generalization.' The speaker draws a faulty conclusion about vegetarians' impact on the environment based on a single, isolated example of a vegetarian who drives a gas-guzzling car. Anecdotal evidence like this cannot be used to make general claims about a larger population."], "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/a84f1d4f78e5ab8b8016.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": ["To prove whether f is a constant function or not, we will use Liouville's theorem. Liouville's theorem states that any bounded entire function (i.e., a function that is holomorphic on the entire complex plane and is bounded) must be constant.\n\nLet's assume that f(z) is not a constant function. Then there exist z1 and z2 in the complex plane C such that f(z1) \u2260 f(z2).\n\nNow, we need to find a bound on |f(z)|. We can start by considering the expression M = max{|f(z1)|, |f(z2)|}. By definition of maxima, |f(z)| \u2264 M for all z in C.\n\nNext, we will find a bounded entire function g(z) that is not constant. Let's define a function g(z) as g(z) = f(z) - 1/f(z2). Since f(z) is not constant, f(z) is not equal to zero anywhere in C. Thus, we can create a new function g(z) that takes into consideration the properties of f(z).\n\nNow let's analyze g(z). Notice that |g(z)| = |f(z) - 1/f(z2)|. We can bound |g(z)| as follows: \n\n1. If |f(z)| \u2265 |f(z2)|, then |g(z)| = |f(z) - 1/f(z2)| \u2264 |f(z)| + 1/|f(z2)| \u2264 M + 1/|f(z2)|, which is a constant since M is a constant and |f(z2)| is also a constant.\n\n2. If |f(z)| < |f(z2)|, then |g(z)| = |f(z) - 1/f(z2)| \u2264 |f(z)| + 1/|f(z2)| \u2264 |f(z2)| + 1/|f(z2)| \u2264 M, which is a constant since |f(z2)| is a constant.\n\nIn either case, we find that g(z) is a bounded entire function. Therefore, by Liouville's theorem, g(z) must be a constant function, which contradicts our assumption that f(z) is not constant.\n\nHence, our assumption that f(z) is not constant was incorrect. Therefore, f(z) must be a constant function."], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["Let f be an entire function such that |f(z)| $\\geq$ 1 for every z in C. Is f is a constant function?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["Let f be an entire function such that |f(z)| $\\geq$ 1 for every z in C. Is f is a constant function?"], "rejected": ["No, the entire function f cannot be a constant function because its modulus or absolute value on the entire complex plane, denoted by |f(z)|, is greater than or equal to 1. This means that f takes values outside the one-point set {c}, where c is any complex number, which is a necessary condition for a function to be a constant. Therefore, f must take at least two distinct values and hence is not a constant function."], "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}