Create README.md

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model:
+  - Qwen/Qwen3-VL-8B-Instruct
+tags:
+  - multimodal
+  - vision-language
+  - tool-use
+  - agentic
+  - qwen3_vl
+  - sft
+datasets:
+  - Accio-Lab/Metis-ColdStart
+language:
+  - en
+pipeline_tag: image-text-to-text
+---
+
+# Metis-8B-ColdStart
+
+**Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models**
+
+Metis-8B-ColdStart is the **SFT (Supervised Fine-Tuning) checkpoint** of the Metis framework, fine-tuned from [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) on the curated [Metis-ColdStart](https://huggingface.co/datasets/Accio-Lab/Metis-ColdStart) dataset. This checkpoint serves as the starting point for HDPO reinforcement learning, which produces the final [Metis-8B-RL](https://huggingface.co/Accio-Lab/Metis-8B-RL) model.
+
+[[Paper (arXiv)]](https://arxiv.org/abs/2604.08545) | [[GitHub]](https://github.com/Accio-Lab/Metis) | [[RL Model]](https://huggingface.co/Accio-Lab/Metis-8B-RL) | [[ColdStart Data]](https://huggingface.co/datasets/Accio-Lab/Metis-ColdStart) | [[RL Data]](https://huggingface.co/datasets/Accio-Lab/Metis-RL)
+
+## Model Details
+
+| Attribute | Value |
+|---|---|
+| Base model | [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) |
+| Training stage | Supervised Fine-Tuning (Cold Start) |
+| Training data | [Metis-ColdStart](https://huggingface.co/datasets/Accio-Lab/Metis-ColdStart) (~27K samples) |
+| Next stage | → [Metis-8B-RL](https://huggingface.co/Accio-Lab/Metis-8B-RL) (HDPO reinforcement learning) |
+| License | Apache-2.0 |
+
+## Cold Start Data Curation Pipeline
+
+The SFT corpus is curated from publicly available tool-augmented multimodal trajectories (DeepEyesV2, V-Interaction, Thyme, OpenMMReasoner) through a rigorous three-stage pipeline:
+
+1. **Eradicating hallucinated environmental dynamics** — Execute all code in a sandbox environment; discard trajectories with execution failures.
+2. **Isolating genuine tool necessity** — Filter out samples where the base model achieves pass@8 = 1 without any tools, ensuring only genuinely tool-dependent samples remain.
+3. **Multidimensional meta-cognitive filtering** — An LLM judge evaluates visual relevance, reasoning coherence, and tool-use rationale to ensure high quality.
+
+## Training Pipeline
+
+```
+Qwen3-VL-8B-Instruct
+        │
+        ▼  SFT on Metis-ColdStart (~27K samples)
+  Metis-8B-ColdStart  ← (this checkpoint)
+        │
+        ▼  HDPO on Metis-RL (~5K prompts)
+   Metis-8B-RL  (final model)
+```
+
+## Usage
+
+Please refer to the [GitHub repository](https://github.com/Accio-Lab/Metis) for full installation and inference instructions.
+
+### Installation
+
+```bash
+git clone https://github.com/Accio-Lab/Metis.git
+cd Metis
+pip install -e verl
+pip install -e ".[vllm,search_tool,python_code_dep]"
+```
+
+## Citation
+
+```bibtex
+@article{yan2026metis,
+  title={Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models},
+  author={Yan, Shilin and Tong, Jintao and Xue, Hongwei and Tang, Xiaojun and Wang, Yangyang and Shi, Kunyu and Zhang, Guannan and Li, Ruixuan and Zou, Yixiong},
+  journal={arXiv preprint arXiv:2604.08545},
+  year={2026}
+}
+```
+
+## Acknowledgments
+
+Metis is built upon [verl](https://github.com/volcengine/verl), [verl-tool](https://github.com/TIGER-AI-Lab/verl-tool), and [Qwen3-VL](https://github.com/QwenLM/Qwen3-VL).