Instructions to use lingcco/EventMemAgent-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lingcco/EventMemAgent-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="lingcco/EventMemAgent-8B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("lingcco/EventMemAgent-8B")
model = AutoModelForMultimodalLM.from_pretrained("lingcco/EventMemAgent-8B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lingcco/EventMemAgent-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lingcco/EventMemAgent-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lingcco/EventMemAgent-8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/lingcco/EventMemAgent-8B

SGLang

How to use lingcco/EventMemAgent-8B 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 "lingcco/EventMemAgent-8B" \
    --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": "lingcco/EventMemAgent-8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "lingcco/EventMemAgent-8B" \
        --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": "lingcco/EventMemAgent-8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use lingcco/EventMemAgent-8B with Docker Model Runner:
```
docker model run hf.co/lingcco/EventMemAgent-8B
```

EventMemAgent-8B

Model for EventMemAgent: Hierarchical Event-Centric Memory for Online Video Understanding with Adaptive Tool Use (ECCV 2026).

Model

EventMemAgent uses a Qwen3-VL-8B-Instruct policy with hierarchical event-centric memory and adaptive memory-retrieval, OCR and object-detection tools. Event captions are generated separately by a frozen Qwen3-VL-4B-Instruct model; event/query embeddings use Qwen3-Embedding-0.6B.

This upload contains the merged actor from training run 1-30_8B, step 150, in standard Hugging Face format. It includes the model, tokenizer, processor and chat template. Optimizer states, training logs and evaluation predictions are not included.

Training

10K MovieChat samples annotated by VideoMarathon; multi-turn GRPO with group size 8, global batch 64, PPO minibatch 32 and per-GPU microbatch 1. AdamW uses a learning rate of 1e-6, five warmup steps, one epoch and no KL loss. Training prompt/response limits are 8192 tokens each, with at most ten assistant turns.

Usage

Download the checkpoint and configure MODEL_PATH in the accompanying code:

hf download lingcco/EventMemAgent-8B --local-dir checkpoints/released/EventMemAgent-8B

Use the code repository's inference and evaluation entry points. The checkpoint alone does not implement streaming memory or provide OCR and detection services; these are separate components of the pipeline.

Paper results

OVO-Bench	StreamingBench
60.75	77.00

Results reported in the paper.

License and limitations

The model is released under Apache 2.0, following its Qwen3-VL-8B-Instruct base model. The research model can produce incorrect answers; memory captions and perception tools can also make errors. It is not intended for safety-critical use.

Downloads last month: -

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for lingcco/EventMemAgent-8B

Base model

Qwen/Qwen3-VL-8B-Instruct

Finetuned

(573)

this model

Paper for lingcco/EventMemAgent-8B

EventMemAgent: Hierarchical Event-Centric Memory for Online Video Understanding with Adaptive Tool Use

Paper • 2602.15329 • Published Feb 17 • 1