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)

# pip install -U transformers accelerate
# 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=256)
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 / README.md

lingcco

Release EventMemAgent model

93be88c verified about 1 month ago

preview code

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2.18 kB

	---
	license: apache-2.0
	base_model: Qwen/Qwen3-VL-8B-Instruct
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- video-understanding
	- reinforcement-learning
	- eventmemagent
	---

	# EventMemAgent-8B

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

	- [Paper](https://arxiv.org/abs/2602.15329)
	- [Code](https://github.com/lingcco/EventMemAgent)
	- [Processed data](https://huggingface.co/datasets/lingcco/EventMemAgent)

	## 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:

	```bash
	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.