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marcusnogueira/shieldgemma-9b-awq-4bit
shieldgemma-9b-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 google/shieldgemma-9b at revision b8b636016df4540721a098c7aab91c97ec6ee508.
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From the Hugging Face model README
Reproducible AWQ 4-bit quantization of
google/shieldgemma-9b at revision
b8b636016df4540721a098c7aab91c97ec6ee508.
2.0.0GPTQModel==7.1.0bfloat1665ea2fdb18470289314defc0fb94e27d4877b62143569615a1ce01745607ce1f1588ec454efa1a09f29cd18ddd04fe05fc8653a242{
"group_size": 128,
"sym": false,
"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.
This checkpoint uses asymmetric AWQ (sym=false, with an explicit zero-point)
because ShieldGemma's Gemma 2 architecture is served in BF16. The zero-point
configuration enables vLLM's BF16-compatible Marlin path; the symmetric AWQ
configuration falls back to the regular FP16-only AWQ kernel and is therefore
incompatible with this model's required inference dtype. It was validated with
vLLM 0.31.0 using --dtype bfloat16 and automatic kernel selection.
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.