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README.md

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 ---
 license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: video-text-to-text
+base_model: OpenMOSS-Team/MOSS-VL-Base-0708
+tags:
+- moss_vl
+- feature-extraction
+- SFT
+- Video-Understanding
+- Image-Understanding
+- MOSS-VL
+- OpenMOSS
+- multimodal
+- video
+- vision-language
+- custom_code
 ---
+
+<p align="center">
+  <img src="assets/logo.png" width="300" alt="MOSS-VL"/>
+</p>
+
+# MOSS-VL-Instruct-0708
+
+## Introduction
+
+MOSS-VL-Instruct-0708 is the instruction-tuned checkpoint of the MOSS-VL 0708 release, part of the OpenMOSS ecosystem for open visual understanding.
+
+Built on top of MOSS-VL-Base-0708 through supervised fine-tuning (SFT), this checkpoint is designed as a high-performance offline multimodal model. It supports image understanding, OCR, document parsing, visual reasoning, instruction following, and video understanding, with particular strength in long-form video comprehension, temporal reasoning, action recognition, and fine-grained event localization.
+
+The 0708 release keeps the MOSS-VL cross-attention design and a 256K text context window while refreshing the data and instruction-tuning recipe for stronger offline multimodal usage.
+
+## Highlights
+
+- Strong video understanding: designed for long videos, temporal reasoning, action recognition, and second-level event localization.
+- General multimodal perception: supports image understanding, fine-grained visual recognition, OCR, and document analysis.
+- Reliable instruction following: SFT aligns the base checkpoint with user instructions across image, video, and text tasks.
+- Open model family: released together with MOSS-VL-Base-0708 for continued pretraining, fine-tuning, and applied research.
+
+## Model Architecture
+
+MOSS-VL-Instruct-0708 adopts a cross-attention-based vision-language architecture that decouples visual encoding from language reasoning. The model processes images, videos, and text in a unified pipeline and uses cross-attention layers to connect language tokens with visual representations.
+
+<p align="center">
+  <img src="assets/architecture.png" alt="MOSS-VL Architecture" width="100%"/>
+</p>
+
+Key configuration details:
+
+| Item | Value |
+| --- | --- |
+| Parameters | 11B |
+| Tensor type | BF16 |
+| Context length | 256K |
+| Vision patch size | 16 |
+| Temporal patch size | 1 |
+| Default video FPS | 1.0 |
+| Default max video frames | 256 |
+
+## Absolute Timestamps
+
+For video inputs, MOSS-VL injects absolute timestamps alongside sampled frames. This helps the model reason about event order, duration, pacing, and temporal localization instead of relying only on frame order.
+
+## Cross-attention RoPE (XRoPE)
+
+MOSS-VL uses Cross-attention Rotary Position Embedding (XRoPE), which maps text tokens and visual patches into a unified three-dimensional coordinate space defined by Time (t), Height (h), and Width (w). This gives the model a consistent positional representation for image and video reasoning.
+
+## Model Performance
+
+MOSS-VL-Instruct-0708 is intended for offline multimodal evaluation across visual perception, multimodal reasoning, OCR/document understanding, and video understanding. Detailed benchmark tables for the 0708 release will be maintained in the MOSS-VL project resources.
+
+<p align="center">
+  <img src="assets/benchmark-offline.png" alt="MOSS-VL Offline Benchmark" width="100%"/>
+</p>
+
+For the previous public checkpoint, see [MOSS-VL-Instruct-0408](https://huggingface.co/OpenMOSS-Team/MOSS-VL-Instruct-0408).
+
+## Quickstart
+
+### Installation
+
+Clone the MOSS-VL repository and install the project requirements:
+
+```bash
+git clone https://github.com/OpenMOSS/MOSS-VL.git
+cd MOSS-VL
+conda create -n moss_vl python=3.12 pip -y
+conda activate moss_vl
+pip install -i https://pypi.org/simple --no-build-isolation -r requirements.txt
+```
+
+### Load Model
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoProcessor
+
+checkpoint = "OpenMOSS-Team/MOSS-VL-Instruct-0708"
+
+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",
+)
+```
+
+### Single-image Inference
+
+```python
+image_path = "data/example_image.jpg"
+prompt = "Describe this image."
+
+text = model.offline_image_generate(
+    processor,
+    prompt=prompt,
+    image=image_path,
+    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,
+    temperature=1.0,
+    top_k=50,
+    top_p=1.0,
+    repetition_penalty=1.0,
+    do_sample=False,
+    vision_chunked_length=64,
+)
+
+print(text)
+```
+
+### Single-video Inference
+
+```python
+video_path = "data/example_video.mp4"
+prompt = "Describe this video."
+
+text = model.offline_video_generate(
+    processor,
+    prompt=prompt,
+    video=video_path,
+    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=256,
+    num_extract_threads=4,
+    image_mean=[0.5, 0.5, 0.5],
+    image_std=[0.5, 0.5, 0.5],
+    max_new_tokens=256,
+    temperature=1.0,
+    top_k=50,
+    top_p=1.0,
+    repetition_penalty=1.0,
+    do_sample=False,
+    vision_chunked_length=64,
+)
+
+print(text)
+```
+
+### Batched Offline Inference
+
+`offline_batch_generate` accepts independent image/video/text queries. Queries in the same batch should share the same `media_kwargs` and `generate_kwargs`.
+
+```python
+queries = [
+    {
+        "prompt": "Describe sample A.",
+        "images": [],
+        "videos": ["data/sample_a.mp4"],
+        "media_kwargs": {
+            "video_fps": 1.0,
+            "min_frames": 8,
+            "max_frames": 256,
+        },
+        "generate_kwargs": {
+            "temperature": 1.0,
+            "top_k": 50,
+            "top_p": 1.0,
+            "max_new_tokens": 256,
+            "repetition_penalty": 1.0,
+            "do_sample": False,
+        },
+    },
+    {
+        "prompt": "Describe sample B.",
+        "images": [],
+        "videos": ["data/sample_b.mp4"],
+        "media_kwargs": {
+            "video_fps": 1.0,
+            "min_frames": 8,
+            "max_frames": 256,
+        },
+        "generate_kwargs": {
+            "temperature": 1.0,
+            "top_k": 50,
+            "top_p": 1.0,
+            "max_new_tokens": 256,
+            "repetition_penalty": 1.0,
+            "do_sample": False,
+        },
+    },
+]
+
+with torch.no_grad():
+    result = model.offline_batch_generate(
+        processor,
+        queries,
+        vision_chunked_length=64,
+    )
+
+texts = [item["text"] for item in result["results"]]
+print(texts)
+```
+
+## Related Checkpoints
+
+| Model | Parameters | Context | Usage | Hugging Face |
+| --- | ---: | ---: | --- | --- |
+| MOSS-VL-Instruct-0708 | 11B | 256K | Offline multimodal instruction following | https://huggingface.co/OpenMOSS-Team/MOSS-VL-Instruct-0708 |
+| MOSS-VL-Base-0708 | 11B | 256K | Continued pretraining and fine-tuning | https://huggingface.co/OpenMOSS-Team/MOSS-VL-Base-0708 |
+| MOSS-VL-Instruct-0408 | 11B | 256K | Previous instruction-tuned checkpoint | https://huggingface.co/OpenMOSS-Team/MOSS-VL-Instruct-0408 |
+| MOSS-VL-Base-0408 | 11B | 256K | Previous base checkpoint | https://huggingface.co/OpenMOSS-Team/MOSS-VL-Base-0408 |
+
+## Limitations and Future Work
+
+MOSS-VL-Instruct-0708 is optimized for general offline multimodal understanding. Very dense videos, highly specialized domains, precise small-text OCR, and tasks requiring strict numerical reasoning may still require task-specific prompting, sampling choices, or fine-tuning.
+
+We are continuing to improve mathematical reasoning, code reasoning, RL post-training, and broader task-specific evaluations for future MOSS-VL releases.
+
+## Citation
+
+```bibtex
+@misc{moss_vl_2026,
+  title         = {{MOSS-VL Technical Report}},
+  author        = {OpenMOSS Team},
+  year          = {2026},
+  howpublished  = {\url{https://github.com/OpenMOSS/MOSS-VL}},
+  note          = {GitHub repository}
+}
+```
+