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| title: Minecraft Agent | |
| emoji: ⛏️ | |
| colorFrom: green | |
| colorTo: blue | |
| sdk: static | |
| pinned: false | |
| models: | |
| - EmberJin/Minecraft-Agent-Qwen3.5-9B-Stage3 | |
| # Minecraft Agent — Qwen3.5-9B (Stage3) | |
| A vision-language agent that plays Minecraft from raw first-person pixels (640×360 @ 20 FPS) and emits structured text actions. Stage-3 SFT on 200k+ human gameplay trajectories (6M frames, 20 FPS) converted to a text-action format. | |
| ## Pages | |
| - **[`index.html`](index.html)** — Model overview, evaluation table, training recipe, action space, and a real annotated trajectory from the training data. | |
| - **[`cot.html`](cot.html)** — Demo dataset: 531 trajectories of decision-point Chain-of-Thought annotations (CoT pilot subset). | |
| ## CoT Demo Dataset | |
| The CoT demo dataset (531 trajectories, 1,917 thoughts) is available under `data/`: | |
| | File | Size | Contents | | |
| |---|---|---| | |
| | `data/thoughts.jsonl` | ~3 MB | Text-only export: instruction + step / thought / action | | |
| | `data/annotated.pkl` | 450 MB | Full trajectories with image bytes (for continue-SFT training) | | |
| Annotation pipeline (in `cot_annotation/`): decision-point detection (R1/R2/R3 rules, ~12.4% trigger rate) → VLM dual-channel prompting → step-alignment + format validation (≤45 words) → rendering. See the model repo's research notes for the design rationale. |