aleada/Gemma-3-12B-it-W4A16

⚠️ Update (2026-07-05) — vLLM loading fixed

An earlier revision of these weights failed to load on vLLM (≤ 0.23.x), raising:

KeyError: embeddings.patch_embedding.bias

Cause. transformers > 4.52.2 changed the multimodal weight-naming convention (to model.vision_tower.* / model.language_model.*), and GPTQModel followed it during quantization — but vLLM still expects the previous naming (vision_tower.vision_model.* / language_model.model.*), so the SigLIP vision tower failed to map (upstream: llm-compressor #1546).

Fix. The tensor keys are remapped to the vLLM-expected convention. The quantized weights are byte-identical — no re-quantization, nothing else changed. Just re-download the safetensors (or re-pull the repo) and it loads.

Verified: loads + runs on vLLM v0.23.0 (TP = 2), and still loads under transformers. Details in the Community tab.

gemma-3-12b-it-qat-q4_0-unquantized — W4A16 (GPTQModel)

Standard W4A16 GPTQ quantization of Lightricks/gemma-3-12b-it-qat-q4_0-unquantized, produced with GPTQModel 7.1.0 inside a reproducible Docker container. The artifact is drop-in loadable by vLLM (≤ 0.23.x, tensor-parallel) — vLLM auto-detects the GPTQ method from the embedded quantization_config at load time. (See the update note above: the vision-tower key naming is now aligned to vLLM's expectation.)

Produced with GPTQModel rather than the llm-compressor path used for the rest of this collection. The source is Google's quantization-aware-trained release restored to BF16 by Lightricks, so the weights entering GPTQ have already been trained to tolerate 4-bit rounding — the vision tower and multimodal projector still stay BF16, as they do across this collection.

Maintained by Alex Adamopoulos at assert.gr as part of an ongoing series of vLLM-friendly quantized packs targeting underserved 2026 models.

Reproducibility

Parameter	Value
Source model	Lightricks/gemma-3-12b-it-qat-q4_0-unquantized
Quantization tool	GPTQModel 7.1.0 (ModelCloud)
bits	4
group_size	128
desc_act	False
sym	True
damp_percent	0.05
damp_auto_increment	0.01
Calibration dataset	allenai/c4
Calibration samples	128
Quantized size	7.87 GiB

GPTQ inverts a Hessian per module, and too little damping makes that inversion fail. GPTQModel starts at a higher damping than most tooling and raises it automatically on failure, so no module here silently degraded to plain round-to-nearest.

License

Inherits the license of the base model. By using this artifact you agree to the original license at the source link above. Atlas / assert.gr adds no additional restrictions on the quantized weights.

Usage with vLLM

docker run --runtime=nvidia --gpus all \
    -p 8000:8000 \
    -e HF_TOKEN=hf_XXX \
    vllm/vllm-openai:latest \
    --model aleada/Gemma-3-12B-it-W4A16 \
    --limit-mm-per-prompt 'image=1' \
    --gpu-memory-utilization 0.92 \
    --enable-prefix-caching

vLLM auto-detects the GPTQ format from the embedded quantization_config.quant_method. Passing --quantization gptq is allowed but redundant. vLLM also picks the model's full native context window from config.json. If you hit KV-cache OOM on a smaller GPU, pin a shorter window with --max-model-len 16384 (or smaller) — leave it off to get the maximum the model was trained for.

Vision encoder + multimodal projector remain BF16 by design — quantizing them gives negligible memory benefit relative to accuracy cost. Only the language tower's Linear weights are W4 GPTQ-quantized. Once vLLM is running, hit it with any OpenAI client:

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
resp = client.chat.completions.create(
    model="aleada/Gemma-3-12B-it-W4A16",
    messages=[{"role": "user", "content": "Hello"}],
)
print(resp.choices[0].message.content)

Hardware target

Requires CUDA compute-capability ≥ 7.5 (Turing+). Verified on NVIDIA RTX 3090 (compute 8.6) where the W4A16 path runs the quantized Linears at INT4 weights / BF16 activations.

Check this pack yourself

Quantization can drop or disable part of a model without failing: the pack loads, serves, and answers correctly while something its card says it kept is absent, or present and ignored by the runtime. Nothing errors, and the card still promises it.

Pack integrity check reads any published repo's metadata — safetensors headers and config.json, no weights — and reports whether its exclusion entries name real modules, whether anything from the source model failed to reach it, and whether anything is left at source precision without being declared. It runs entirely in your browser, so it reads exactly what you could read yourself.

Point it at this pack. Point it at someone else's.

About the maintainer

Alex Adamopoulos is the founder of assert.gr and the engineer behind the atlas self-evolving AI agent platform.

Connect:

• HuggingFace: @aleada
• Company: assert.gr