GoodStartLabs
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gin-rummy-hbc-qwen3.5-0.8b

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 ---
+language:
+- en
+license: apache-2.0
+tags:
+- gin-rummy
+- card-games
+- behavioral-cloning
+- reinforcement-learning
+- game-ai
+base_model: Qwen/Qwen3.5-0.8B
+datasets:
+- GoodStartLabs/gin-rummy-trajectories-32k
+metrics:
+- accuracy
+pipeline_tag: text-generation
 ---
+# Gin Rummy HBC - Qwen3.5 0.8B
+**Behavioral cloning model for Gin Rummy trained via supervised fine-tuning on expert trajectories.**
+This model was trained on 32,000 stratified expert game states to learn optimal Gin Rummy decision-making. It serves as the initialization for subsequent GRPO (Group Relative Policy Optimization) self-play training.
 ## Model Details
+- **Model type:** Causal language model (decoder-only transformer)
+- **Base model:** [Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B)
+- **Parameters:** 0.8B parameters
+- **Training method:** LoRA (Low-Rank Adaptation) fine-tuning
+- **Task:** Gin Rummy move prediction
+- **License:** Apache 2.0
+## Training Data
+**Dataset:** [GoodStartLabs/gin-rummy-trajectories-32k](https://huggingface.co/datasets/GoodStartLabs/gin-rummy-trajectories-32k)
+- **Training samples:** 32,000 (stratified sampling, minimum 1,000 per action type)
+- **Validation samples:** 1,000 (perfectly balanced, 200 per action type)
+- **Source:** Expert agent gameplay using Monte Carlo Tree Search (MCTS)
+**Action distribution (training set):**
+- `discard` (discard a card): 44.6%
+- `draw` (draw from stock): 33.1%
+- `+discard` (pick from discard pile): 14.9%
+- `KNOCK-[card]` (knock and discard): 4.0%
+- `pass` (pass on upcard): 3.5%
+**Validation set:** Perfectly balanced with exactly 200 samples per action type for unbiased evaluation.
+## Training Procedure
+**Fine-tuning platform:** Together AI (serverless LoRA training)
+**Hyperparameters:**
+- LoRA rank: 16 (0.8B, 2B) / 32 (4B)
+- LoRA alpha: 16 (0.8B, 2B) / 32 (4B)
+- LoRA dropout: 0.05
+- LoRA modules: all-linear
+- Learning rate: 1e-4 (0.8B) / 5e-5 (2B, 4B)
+- Batch size: 8
+- Epochs: 3
+- Warmup ratio: 0.1
+- Weight decay: 0.01
+- Max gradient norm: 1.0
+- **Train on inputs:** False (loss calculated only on assistant response tokens)
+**Training duration:** ~2-4 hours per model
+**Infrastructure:**
+- Platform: Together AI
+- GPUs: NVIDIA H100 (serverless)
+- Precision: bfloat16
+## Intended Use
+### Primary Use Case
+This model serves as the **warm-start initialization** for GRPO self-play training:
+1. **HBC (Behavioral Cloning)** ← *This model*
+   - Learn from expert trajectories
+   - Acquire strong baseline policy
+   - Fast convergence to competent play
+2. **GRPO (Group Relative Policy Optimization)** ← *Next stage*
+   - Self-play reinforcement learning
+   - Discover novel strategies
+   - Optimize for win rate
+### Inference
+The model predicts the next action given the current game state formatted as a chat conversation:
+**Input format:**
+```
+[SYSTEM]
+You are an expert Gin Rummy player. Your goal is to minimize deadwood and form melds.
+[USER]
+History:
+1. You: +D6x -C3
+2. Opp: draw -CK
+Now:
+Hand: CK D2 D3 D4 D5 D6 D9 H7 HK HQ S9
+Stock: 28 | Deadwood: 45 | Phase: discard_or_knock
+YOUR TURN | Can: no
+[ASSISTANT]
+```
+**Output (predicted action):**
+```
+-H7
+```
+**Action format:**
+- `draw` - Draw from stock pile
+- `+discard` - Pick from discard pile
+- `-[CARD]` - Discard a card (e.g., `-H7` = discard 7 of Hearts)
+- `KNOCK-[CARD]` - Knock and discard (e.g., `KNOCK-C3`)
+- `pass` - Pass on the initial upcard
+**Card notation:** Rank (A/2-9/T/J/Q/K) + Suit (C/D/H/S)
+- Example: `H7` = 7 of Hearts, `CK` = King of Clubs, `SA` = Ace of Spades
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+# Load model and tokenizer
+model_name = "GoodStartLabs/gin-rummy-hbc-qwen3.5-0.8b"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype="auto",
+)
+# Format game state as chat
+messages = [
+    {
+        "role": "system",
+        "content": "You are an expert Gin Rummy player. Your goal is to minimize deadwood and form melds."
+    },
+    {
+        "role": "user",
+        "content": '''History:
+1. Opp: draw -SQ
+2. You: draw(DT) -DT
+Now:
+Hand: C9 D3 D9 H3 H6 HJ HQ HT S6 S9
+Stock: 22 | Deadwood: 18 | Phase: draw
+YOUR TURN | Can: no'''
+    }
+]
+# Generate prediction
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=20,
+    temperature=0.0,  # Greedy decoding for deterministic play
+    do_sample=False,
+)
+action = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True).strip()
+print(f"Predicted action: {action}")
+```
+## Limitations
+- **Behavioral cloning ceiling:** Model is limited by the quality of expert demonstrations. Cannot exceed expert performance without RL.
+- **Distribution shift:** May struggle on game states not represented in training data.
+- **Stochastic policy:** Model predicts a distribution over actions; greedy decoding gives deterministic play but may not explore optimally.
+- **No opponent modeling:** Does not explicitly model opponent strategy (though may learn implicit patterns from game history).
+- **Fixed strategy:** Cannot adapt during a game; uses the same policy throughout.
 ## Evaluation
+**Validation accuracy (on balanced 1K validation set):**
+- Overall: *TBD* (check W&B: [good-start-labs/gin-rummy-hbc](https://wandb.ai/good-start-labs/gin-rummy-hbc))
+- Per action type: *TBD*
+**Win rate vs. baselines:**
+- Random policy: *TBD*
+- Greedy heuristic: *TBD*
+- Expert policy: *TBD*
+## Ethical Considerations
+This model is trained for the game of Gin Rummy and should only be used for:
+- Game AI research
+- Educational purposes
+- Entertainment (single-player practice, AI opponents)
+**Not intended for:**
+- Real-money gambling
+- Cheating in online games
+- Deceptive or manipulative applications
+## Citation
+If you use this model in your research, please cite:
+```bibtex
+@misc{gin-rummy-hbc-0.8b,
+  author = {Good Start Labs},
+  title = {Gin Rummy HBC - Qwen3.5 0.8B},
+  year = {2026},
+  publisher = {HuggingFace},
+  howpublished = {\url{GoodStartLabs/gin-rummy-hbc-qwen3.5-0.8b}},
+}
+```
+## Model Card Authors
+- Good Start Labs
+- Contact: [GitHub](https://github.com/GoodStartLabs)
+## Model Card Contact
+For questions or issues with this model:
+- Open an issue on the [model repository](https://huggingface.co/GoodStartLabs/gin-rummy-hbc-qwen3.5-0.8b)
+- Check [W&B training logs](https://wandb.ai/good-start-labs/gin-rummy-hbc)
+---
+*Model trained on Together AI • Base model: Qwen3.5 • Training date: March 2026*