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Model: GGOSinon/babyai-world-model-7B-sft
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+  - world-model
+  - babyai
+  - reinforcement-learning
+  - model-based-rl
+  - sft
+  - lora
+datasets:
+  - GGOSinon/babyai-world-model-sft
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# BabyAI World Model (Qwen2.5-7B SFT)
+
+A world model for the BabyAI grid-world environment, fine-tuned from Qwen2.5-7B-Instruct using LoRA. This model predicts the next observation and available actions given the current state and the agent's action.
+
+## Model Details
+
+- **Base model**: Qwen2.5-7B-Instruct
+- **Fine-tuning**: LoRA (40.4M trainable params, 0.53% of 7.66B total), merged after training
+- **Training data**: [GGOSinon/babyai-world-model-sft](https://huggingface.co/datasets/GGOSinon/babyai-world-model-sft) (58K transitions, 1 epoch)
+- **Training time**: ~5.5 hours on 1x A100 40GB
+- **Final loss**: 0.023
+
+## Performance (Done-Detection, 102 test cases)
+
+| Model | Accuracy | Precision | Recall | FPR |
+|---|---|---|---|---|
+| Qwen2.5-7B zero-shot | 76.5% | 91.7% | 32.4% | 1.5% |
+| **Qwen2.5-7B SFT (this model)** | **97.1%** | **100%** | **91.2%** | **0.0%** |
+| Gemini 2.5 Flash zero-shot | 97.1% | 100% | 91.2% | 0.0% |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("GGOSinon/babyai-world-model-7B-sft", torch_dtype="bfloat16").to("cuda")
+tokenizer = AutoTokenizer.from_pretrained("GGOSinon/babyai-world-model-7B-sft")
+
+messages = [
+    {"role": "system", "content": "You are a simulator for a grid-world environment called BabyAI..."},
+    {"role": "user", "content": "Goal: pick up the red box\n\nObservation:\n...\nAvailable actions: [...]\nAgent's action: pickup red box 1"}
+]
+
+inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt", add_generation_prompt=True).to("cuda")
+output = model.generate(inputs, max_new_tokens=300, do_sample=False)
+print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+## Output Format
+
+The model outputs in this format:
+```
+<observation>next observation text</observation>
+<available_actions>["action1", "action2", ...]</available_actions>
+```
+
+Task completion is indicated by "The task is completed." appended to the observation text.