OpenMOSS-Team/MOSS-VL-Realtime-FP8

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MOSS-VL-Realtime FP8 Dynamic + Transformers KV8

This is the Transformers FP8 release of MOSS-VL-Realtime. It preserves the timestamp-aware streaming interface for frame-by-frame video inference. This checkpoint is not an SGLang release.

Architecture

Quantization profile

Component	Format
252 self-attention/MLP Linear layers in 36 non-cross language layers	compressed-tensors FP8 E4M3 weights with channel-wise static scales and per-token dynamic FP8 input activations
12 cross-attention language layers	BF16
Vision encoder and merger	BF16
Embeddings, norms and lm_head	BF16
Transformers KV cache	HQQ INT8, group size 64, BF16 residual length 128
Attention backend	FlashAttention 2

generation_config.json enables HQQ KV8 automatically. Load the checkpoint directly and do not pass a second quantization configuration or replace its generation config with the BF16 source file.

Quantization benchmark

The final evaluation compares the original BF16 model with all four release profiles on their corresponding benchmark suites. For this streaming FP8 checkpoint, the scores are 70.66 on OVOBench Avg, 62.93 on StreamingBench Avg, and 65.50 on OmniMMI PA, compared with 70.86, 62.42, and 66.00 for BF16.

Hardware requirements

The validated 30-frame streaming test peaked at 25,522 MiB of process VRAM and 26,249 MiB total GPU memory, including a 727 MiB baseline. Use an NVIDIA GPU with more than 26 GiB available memory, or allow Transformers to shard the model across multiple GPUs with device_map="auto".

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 \
  compressed-tensors==0.14.0.1 \
  hqq==0.2.8.post1
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
compressed-tensors	0.14.0.1
HQQ	0.2.8.post1

Video decoding also requires FFmpeg in PATH.

Load the model

import torch
from transformers import AutoModelForCausalLM, AutoProcessor

checkpoint = "OpenMOSS-Team/MOSS-VL-Realtime-FP8"

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()

Realtime streaming inference

Supply PIL-compatible frames with non-decreasing timestamps. One model instance supports one active realtime session.

import time
from PIL import Image

session = model.create_realtime_session(
    processor,
    initial_prompt=(
        "Describe important changes in the video as they happen. "
        "Stay silent when there is no meaningful update."
    ),
    frame_queue_size=1,
    max_tokens_per_turn=12,
    max_new_tokens=4096,
    do_sample=False,
)

frame_paths = [
    "data/frame_0001.jpg",
    "data/frame_0002.jpg",
    "data/frame_0003.jpg",
]

try:
    session.start()
    for index, frame_path in enumerate(frame_paths):
        image = Image.open(frame_path).convert("RGB")
        session.push_frame(image, timestamp=float(index))
        while True:
            chunk = session.poll_output(timeout=0.0)
            if chunk is None:
                break
            print(chunk, end="", flush=True)
        time.sleep(1.0)
finally:
    session.close()

The model may emit control tokens such as <|silence|>, <|round_start|>, and <|round_end|>; applications should filter or render them according to their protocol.

Validated reproduction

The fixed validation used a Xinjiang aerial video at 1 FPS with 30 timestamped frames. It completed 30/30 frames and produced a relevant Chinese tour-guide description.

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/quant/benchmark_mossvl_quant.py \
  --label streaming_fp8_reproduce \
  --checkpoint /inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/final_release/quant/MOSS-VL-0708-Streaming-FP8-Dynamic-KV8-HQQ \
  --gpu 0 \
  --frames 30 \
  --attention-backend flash_attention_2 \
  --timeout 300

Full inputs, commands and raw logs:

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

Configuration files

• config.json: model and FP8 weight/activation configuration.
• generation_config.json: Transformers HQQ KV8 configuration.
• recipe.yaml: compressed-tensors quantization recipe.
• modeling_moss_vl.py: checkpoint-local streaming and quantized-cache code.