Qwen2.5 VL 3B Instruct W4A16 Generic
Quantized with the NOVA quantization pipeline on 2026-04-27. Base model: Qwen/Qwen2.5-VL-3B-Instruct
Source du modèle
- Type
- Text model
- Taille du fichier
- 3,16 GB
- Modèle de base
- Qwen/Qwen2.5-VL-3B-Instruct
- Version
- vmodel
- Mis à jour
- 24 oct. 2026
- Éditeur
- @Mohaaxa
- Téléchargements
- 21
- Paramètres
- 3B
- Source
- Hugging Face
Description de la source
Qwen2.5-VL-3B-Instruct-W4A16-generic
Quantized with the NOVA quantization pipeline on 2026-04-27. Base model: Qwen/Qwen2.5-VL-3B-Instruct
Quantization details
| Parameter | Value |
|---|---|
| Method | W4A16 |
| Group size | 128 |
| Calibration | generic |
| Ignored modules | re:.*lm_head, re:.*visual.* |
| Tool | llm-compressor >= 0.4.2 |
Benchmark results
| Metric | Value |
|---|---|
| Perplexity (wikitext-2, 20 samples) | 20.466 |
| OCR sanity check | ✅ PASS |
| Tokens / second | 1.1 |
| TTFT (exact, prefill only) | 1094.5 ms |
| TPOT (exact, per output token) | 915.9 ms |
| Inference VRAM | 9.15 GB |
| Disk size | 3.41 GB |
TTFT and TPOT measured with
BaseStreamerinjection (prompt-skip corrected).
Registry notes
- Use llm-compressor==0.5.1 + transformers==4.51.3 for quantization.
- Use vLLM>=0.7.2 for inference — Marlin kernels active on Ampere.
- Projector (model.visual.merger) kept at FP32 — matched by visual.* regex.
- OCR and bbox grounding regress 5x faster than MMMU under aggressive quant.
- Keep merger at FP32, not BF16, for best bbox coordinate precision.
- W8A8 requires A100 for calibration — activation statistics need >24GB VRAM.
Usage
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
import torch
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Mohaaxa/Qwen2.5-VL-3B-Instruct-W4A16-generic",
torch_dtype=torch.bfloat16,
device_map="auto",
)
processor = AutoProcessor.from_pretrained("Mohaaxa/Qwen2.5-VL-3B-Instruct-W4A16-generic")
Citation
If you use this model in research, please cite the NOVA project.
Pipeline source: Mohaaxa/nova-quant-pipeline
Sources
1 source
Sources
1 sourceVérifié 24 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- base_model: Qwen/Qwen2.5-VL-3B-Instruct tags: - quantized - w4a16 - robotics - nova-robot pipeline_tag: image-text-to-text language: - en --- # Qwen2.5-VL-3B-Instruct-W4A16-generic Quantized with the NOVA quantization pipeline on 2026-04-27. Base model: [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) ## Quantization details | Parameter | Value | |---|---| | Method | `W4A16` | | Group size | 128 | | Calibration | `generic` | | Ignored modules | `re:.*lm_head, re:.*visual.*` | | Tool | `llm-compressor >= 0.4.2` | ## Benchmark results | Metric | Value | |---|---| | Perplexity (wikitext-2, 20 samples) | 20.466 | | OCR sanity check | ✅ PASS | | Tokens / second | 1.1 | | TTFT (exact, prefill only) | 1094.5 ms | | TPOT (exact, per output token) | 915.9 ms | | Inference VRAM | 9.15 GB | | Disk size | 3.41 GB | > TTFT and TPOT measured with `BaseStreamer` injection (prompt-skip corrected). ## Registry notes - Use llm-compressor==0.5.1 + transformers==4.51.3 for quantization. - Use vLLM>=0.7.2 for inference — Marlin kernels active on Ampere. - Projector (model.visual.merger) kept at FP32 — matched by visual.* regex. - OCR and bbox grounding regress 5x faster than MMMU under aggressive quant. - Keep merger at FP32, not BF16, for best bbox coordinate precision. - W8A8 requires A100 for calibration — activation statistics need >24GB VRAM. ## Usage ```python from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor import torch model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Mohaaxa/Qwen2.5-VL-3B-Instruct-W4A16-generic", torch_dtype=torch.bfloat16, device_map="auto", ) processor = AutoProcessor.from_pretrained("Mohaaxa/Qwen2.5-VL-3B-Instruct-W4A16-generic") ``` ## Citation If you use this model in research, please cite the NOVA project. Pipeline source: `Mohaaxa/nova-quant-pipeline`
Source context: 21 downloads · 1 likes · Pipeline image-text-to-text · Repo Mohaaxa/Qwen2.5-VL-3B-Instruct-W4A16-generic
Source context: 56 downloads · 1 likes · Pipeline image-text-to-text · Repo Mohaaxa/Qwen2.5-VL-3B-Instruct-W4A16-generic
