OpenMOSS-Team/MOSS-VL-Instruct-0708-NF4

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MOSS-VL-Instruct-0708 W4A16 NF4

This is the Transformers NF4 release of MOSS-VL-Instruct-0708. It supports image and video inference through the standard MOSS-VL offline inference path. This checkpoint is not an SGLang release.

Architecture

Quantization profile

Component	Format
240 eligible Linear layers in language layers 4-43	bitsandbytes NF4 weight-only quantization with double quantization and BF16 compute
First four and last four language layers	BF16
Cross-attention projection modules	BF16
Vision encoder and merger	BF16
Embeddings, norms and lm_head	BF16
Transformers KV cache	BF16
Attention backend	FlashAttention 2

The checkpoint carries its bitsandbytes configuration. Load it directly and do not add a second runtime quantization configuration. This variant does not enable HQQ KV8; generation_config.json uses the standard BF16 KV cache.

Quantization benchmark

The final evaluation compares the original BF16 model with all four release profiles on their corresponding benchmark suites. This offline NF4 checkpoint scores 89.53 on DocVQA, 67.30 on VideoMME, 75.86 on MLVU_dev, 51.00/48.17/59.33 on the three TimeLens subsets, and 61.76 on VSIBench.

Hardware requirements

The validated image test peaked at 12,494 MiB of process VRAM. The 1 FPS, maximum-32-frame video test peaked at 16,708 MiB. A single NVIDIA GPU with 24 GB of VRAM is sufficient for the validated profile.

Environment

Installation

git clone https://github.com/OpenMOSS/MOSS-VL.git
cd MOSS-VL

conda create -n moss_vl_quant python=3.12 pip -y
conda activate moss_vl_quant
pip install -i https://pypi.org/simple --no-build-isolation -r requirements.txt
pip install -i https://pypi.org/simple bitsandbytes==0.49.2
python -m pip check

Validated core versions:

Package	Version
Python	3.12.8
PyTorch	2.8.0 + CUDA 12.8
Transformers	4.57.1
Accelerate	1.12.0
FlashAttention	2.8.1
bitsandbytes	0.49.2

Video decoding also requires FFmpeg in PATH.

Load the model

import torch
from transformers import AutoModelForCausalLM, AutoProcessor

checkpoint = "OpenMOSS-Team/MOSS-VL-Instruct-0708-NF4"

processor = AutoProcessor.from_pretrained(
    checkpoint,
    trust_remote_code=True,
    frame_extract_num_threads=1,
)
model = AutoModelForCausalLM.from_pretrained(
    checkpoint,
    trust_remote_code=True,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
)
model.eval()

Image inference

text = model.offline_image_generate(
    processor,
    prompt="Describe this image.",
    image="data/example_image.jpg",
    shortest_edge=4096,
    longest_edge=16777216,
    multi_image_max_pixels=201326592,
    patch_size=16,
    temporal_patch_size=1,
    merge_size=2,
    image_mean=[0.5, 0.5, 0.5],
    image_std=[0.5, 0.5, 0.5],
    max_new_tokens=256,
    do_sample=False,
    vision_chunked_length=64,
)
print(text)

Video inference

text = model.offline_video_generate(
    processor,
    prompt="Describe this video.",
    video="data/example_video.mp4",
    shortest_edge=4096,
    longest_edge=16777216,
    video_max_pixels=201326592,
    patch_size=16,
    temporal_patch_size=1,
    merge_size=2,
    video_fps=1.0,
    min_frames=1,
    max_frames=32,
    num_extract_threads=4,
    image_mean=[0.5, 0.5, 0.5],
    image_std=[0.5, 0.5, 0.5],
    max_new_tokens=256,
    do_sample=False,
    vision_chunked_length=64,
)
print(text)

Validated reproduction

The official runner passed both the receipt image and the 1 FPS Starbucks video tests:

source /inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/quant/venv/bin/activate

/inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/quant/venv/bin/python \
  /inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/final_release/mossvl-github/MOSS-VL/inference/run_inference.py \
  --checkpoint /inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/final_release/quant/MOSS-VL-0708-Offline-NF4-Keep4-KV16 \
  --mode image \
  --input /inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/quant/transformers_validation_20260811/inputs/offline_image.json \
  --output /inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/quant/transformers_validation_20260811/results/offline_nf4_image_output.json \
  --timeout-seconds 300

/inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/quant/venv/bin/python \
  /inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/final_release/mossvl-github/MOSS-VL/inference/run_inference.py \
  --checkpoint /inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/final_release/quant/MOSS-VL-0708-Offline-NF4-Keep4-KV16 \
  --mode video \
  --input /inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/quant/transformers_validation_20260811/inputs/offline_video.json \
  --output /inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/quant/transformers_validation_20260811/results/offline_nf4_video_output.json \
  --timeout-seconds 300

Full inputs, commands and raw results:

/inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/quant/transformers_validation_20260811

Configuration files

• config.json: model and bitsandbytes NF4 configuration.
• generation_config.json: standard generation settings with BF16 KV cache.
• modeling_moss_vl.py: checkpoint-local offline MOSS-VL code.