Instructions to use longvideoagent/longvideoagent-qwen2.5-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use longvideoagent/longvideoagent-qwen2.5-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="longvideoagent/longvideoagent-qwen2.5-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("longvideoagent/longvideoagent-qwen2.5-7b")
model = AutoModelForCausalLM.from_pretrained("longvideoagent/longvideoagent-qwen2.5-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use longvideoagent/longvideoagent-qwen2.5-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "longvideoagent/longvideoagent-qwen2.5-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "longvideoagent/longvideoagent-qwen2.5-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/longvideoagent/longvideoagent-qwen2.5-7b

SGLang

How to use longvideoagent/longvideoagent-qwen2.5-7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "longvideoagent/longvideoagent-qwen2.5-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "longvideoagent/longvideoagent-qwen2.5-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "longvideoagent/longvideoagent-qwen2.5-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "longvideoagent/longvideoagent-qwen2.5-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use longvideoagent/longvideoagent-qwen2.5-7b with Docker Model Runner:
```
docker model run hf.co/longvideoagent/longvideoagent-qwen2.5-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LongVideoAgent Qwen2.5-7B

This repository hosts the released LLM checkpoint for LongVideoAgent, a multi-agent framework for long-video question answering. This model is a Qwen2.5-7B-based checkpoint used in the LongVideoAgent project.

Project links:

Project page: https://longvideoagent.github.io/
Code: https://github.com/longvideoagent/LongVideoAgent
Paper: https://arxiv.org/abs/2512.20618

Overview

LongVideoAgent decomposes long-video reasoning into multiple roles:

MasterAgent for planning and answer generation
GroundingAgent for subtitle-based temporal grounding
VisionAgent for local visual evidence extraction

This checkpoint is intended for use with the official LongVideoAgent codebase and evaluation pipeline.

LongTVQA+ Performance

Compared with the Qwen2.5-7B-Instruct baseline, this checkpoint improves LongTVQA+ accuracy by 6.66 percentage points.

Model	LongTVQA+ Acc	Delta
Qwen2.5-7B-Instruct	57.33%	-
LongVideoAgent Qwen2.5-7B	64.00%	+6.66%

Intended Use

Use this model for:

Research on long-video question answering
Reproducing LongVideoAgent experiments
Studying agentic reasoning over long videos

This checkpoint is not a general-purpose video model by itself. For inference and evaluation, please use the official repository:

https://github.com/longvideoagent/LongVideoAgent

Usage

Please follow the setup and inference instructions in the official repository and project documentation:

If you use this checkpoint in your work, please also cite the LongVideoAgent paper below.

Citation

@misc{liu2025longvideoagentmultiagentreasoninglong,
  title={LongVideoAgent: Multi-Agent Reasoning with Long Videos},
  author={Runtao Liu and Ziyi Liu and Jiaqi Tang and Yue Ma and Renjie Pi and Jipeng Zhang and Qifeng Chen},
  year={2025},
  eprint={2512.20618},
  archivePrefix={arXiv},
  primaryClass={cs.AI},
  url={https://arxiv.org/abs/2512.20618},
}