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  1. README.md +21 -0
  2. config.json +46 -0
  3. pytorch_model.bin +3 -0
README.md ADDED
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+ ---
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+ base_model:
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+ - medmekk/Llama-3.2-3B-Instruct-torchao-int8da8w
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+ ---
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+
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+ # medmekk/Llama-3.2-3B-Instruct-torchao-int8da8w (Quantized)
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+
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+ ## Description
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+ This model is a quantized version of the original model `medmekk/Llama-3.2-3B-Instruct-torchao-int8da8w`. It has been quantized using int8_dynamic_activation_int8_weight quantization with torchao.
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+
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+ ## Quantization Details
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+ - **Quantization Type**: int8_dynamic_activation_int8_weight
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+ - **Group Size**: None
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+
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+ ## Usage
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+ You can use this model in your applications by loading it directly from the Hugging Face Hub:
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+
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+ ```python
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+ from transformers import AutoModel
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+
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+ model = AutoModel.from_pretrained("medmekk/Llama-3.2-3B-Instruct-torchao-int8da8w")
config.json ADDED
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+ {
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+ "_name_or_path": "meta-llama/Llama-3.2-3B-Instruct",
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+ "architectures": [
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+ "LlamaModel"
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 128000,
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+ "eos_token_id": [
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+ 128001,
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+ 128008,
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+ 128009
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+ ],
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+ "head_dim": 128,
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+ "hidden_act": "silu",
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+ "hidden_size": 3072,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 8192,
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+ "max_position_embeddings": 131072,
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+ "mlp_bias": false,
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+ "model_type": "llama",
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+ "num_attention_heads": 24,
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+ "num_hidden_layers": 28,
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+ "num_key_value_heads": 8,
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+ "pretraining_tp": 1,
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+ "quantization_config": {
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+ "modules_to_not_convert": null,
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+ "quant_method": "torchao",
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+ "quant_type": "int8_dynamic_activation_int8_weight",
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+ "quant_type_kwargs": {}
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+ },
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+ "rms_norm_eps": 1e-05,
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+ "rope_scaling": {
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+ "factor": 32.0,
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+ "high_freq_factor": 4.0,
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+ "low_freq_factor": 1.0,
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+ "original_max_position_embeddings": 8192,
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+ "rope_type": "llama3"
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+ },
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+ "rope_theta": 500000.0,
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+ "tie_word_embeddings": true,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.48.0.dev0",
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+ "use_cache": true,
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+ "vocab_size": 128256
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+ }
pytorch_model.bin ADDED
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