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marcusnogueira/llama-guard-3-8b-awq-4bit
llama-guard-3-8b-awq-4bit is a machine learning model from marcusnogueira. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Reproducible AWQ 4-bit quantization of meta-llama/Llama-Guard-3-8B at revision 7327bd9f6efbbe6101dc6cc4736302b3cbb6e425.
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How the weights are stored.
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From the Hugging Face model README
Reproducible AWQ 4-bit quantization of
meta-llama/Llama-Guard-3-8B at revision
7327bd9f6efbbe6101dc6cc4736302b3cbb6e425.
2.0.0GPTQModel==7.1.0bfloat1665ea2fdb18470289314defc0fb94e27d4877b62143569615a1ce01745607ce1f1588ec454efa1a09f29cd18ddd04fe05fc8653a242{
"group_size": 128,
"sym": true,
"desc_act": false,
"true_sequential": true,
"lm_head": false,
"pack_dtype": "int32",
"format": "gemm",
"scale_search_chunked_activations": true,
"bits": 4
}
{
"batch_size": 4,
"calibration_sort": "desc",
"calibration_concat_size": null,
"calibration_data_min_length": 10,
"calibration_concat_separator": null
}
The complete runtime environment, resolved checkpoint metadata, and file hashes
are stored in runtime_environment.json, quantization_manifest.json, and
SHA256SUMS.
The checkpoint that preceded this reproducible build is preserved under the
v1-original tag. Consumers that require an immutable dependency should pin a
tag or commit SHA instead of relying on main.