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Model Details
Model Provider
simaai
Model Tree
stribomon/gemma3-siglip448
Input Modalities
Output Modalities
Supported Functionality
| Component | Method | Weight format | Effective targets |
|---|---|---|---|
Decoder Linear layers and lm_head | GPTQ | symmetric INT4, group size 256, static act-order | 239 |
| Vision encoder Linear layers | RTN | symmetric INT8, per-channel | 162 |
| Projector | none present | source precision | Explicitly recorded in recipe.yaml |
The lm_head follows the default GPTQ INT4/G256 policy; no INT8 escalation was
made. Text GPTQ calibration used lmms-lab/flickr30k test[:512],
deterministic dataset order, 512 image-text samples, sequence length 2048,
batch size 1, no concatenation, and no padding to maximum length. Vision RTN
does not use Hessian calibration. The exact resolved targets and effective
settings are saved in recipe.yaml.
Full MMStar used all 1,500 examples, VLMEvalKit commit
7055d3010c38ccb5dcae1bc9535ca19c7fe5d79f, deterministic generation, and
exact_matching without an API judge.
| Checkpoint | Overall accuracy |
|---|---|
| Source | 42.8000% |
| Existing text GPTQ + vision GPTQ checkpoint | 42.4000% |
| This text GPTQ + vision RTN checkpoint | 43.0667% |
| RTN change versus vision GPTQ | +0.6667 percentage points |
| RTN relative change versus vision GPTQ | +1.5723% |
Evaluation date: 2026-07-17. Slurm jobs: source 4684, vision-GPTQ baseline
4685, and this vision-RTN candidate 4737. The comparison is recorded in
vlmeval_results/mmstar_results_vision_rtn.csv; raw predictions and status
files remain under vlmeval_results/mmstar_full_vision_rtn/.
Validation: MMStar inference passed (1,500/1,500).
Full matched WikiText-2 word perplexity (EleutherAI/wikitext_document_level, wikitext-2-raw-v1, no example limit; 2026-07-18): source 23.0695; this checkpoint 31.3585; absolute degradation +8.2890; relative degradation +35.93%. Raw JSON evidence: perplexity_results/vlm_full_wikitext/gemma3_siglip448_{source_v2,gptq_w4g256_visionrtn_w8_v3}.json.
This directory contains the exact quantize.py, recipe.yaml, and
versions.txt used for the artifact:
bash
python quantize.py \--model-path /path/to/source-model \--output-dir /path/to/new-quantized-model
text
Python: 3.13.2torch: 2.11.0+cu128CUDA: 12.8transformers: 5.10.1llm-compressor: 0.12.0auto-round: 0.13.0compressed-tensors: 0.17.1
This is the pre-LLiMa quantized Hugging Face artifact. LLiMa compiler output must remain a separate deployment artifact unless its format and redistribution are approved. No Hugging Face upload is authorized for this batch.
Quantization quality can vary by language, visual domain, prompt format, context length, and downstream runtime. MMStar does not replace application-specific validation. Users must validate their own deployment and compiler/runtime compatibility.