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lingcco
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Model Details
Model Provider
lingcco
Model Tree
Qwen/Qwen3-VL-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
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.
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.
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.
| OVO-Bench | StreamingBench |
|---|---|
| 60.75 | 77.00 |
Results reported in the paper.
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.