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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

TipoText modelModelo baseQwen/Qwen2.5-VL-3B-InstructVersiónmodelPublicador@MohaaxaCcalidad

Fuente del modelo

Tipo
Text model
Tamaño del archivo
3,16 GB
Modelo base
Qwen/Qwen2.5-VL-3B-Instruct
Versión
vmodel
Actualizado
24 oct 2026
Publicador
@Mohaaxa
Descargas
21
Parámetros
3B
Fuente
Hugging Face

Descripción de la fuente

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

ParameterValue
MethodW4A16
Group size128
Calibrationgeneric
Ignored modulesre:.*lm_head, re:.*visual.*
Toolllm-compressor >= 0.4.2

Benchmark results

MetricValue
Perplexity (wikitext-2, 20 samples)20.466
OCR sanity check✅ PASS
Tokens / second1.1
TTFT (exact, prefill only)1094.5 ms
TPOT (exact, per output token)915.9 ms
Inference VRAM9.15 GB
Disk size3.41 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

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

Fuentes

1 fuente
Hugging FacePrincipalVersión d0074dfa89bf417979941d165e583c5f782cf13b
huggingface.co/Mohaaxa/Qwen2.5-VL-3B-Instruct-W4A16-generic/tree/d0074dfa89bf417979941d165e583c5f782cf13b

Verificado 24 oct

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 3,16 GB · SHA-256 5fb1da03c8f5…38c0 · Hugging Face

Download file

Extractos de fuentes

3 extractos

--- 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