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---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: peft
tags:
- chess
- tinyllama
- lora
- json
- alpaca-format
- ai-tournament
- aura
---
# ♟️ Konvah's Chess TinyLlama
This model is a fine-tuned version of [`TinyLlama/TinyLlama-1.1B-Chat-v1.0`](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) using LoRA for the **Aura Chess AI Tournament**. It predicts high-quality chess moves in JSON format, given a move history, color, and a list of legal moves.
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## 🧠 Model Objective
The model learns to:
- Choose the best legal move (`move`)
- Give a short explanation (`reasoning`) in ≤10 words
- Format responses as valid JSON
- Respond in `[INST] ... [/INST]` format
---
## 💡 Input Format
The model uses structured prompts:
```json
[INST]
You are a chess player.
{"moveHistory": ["e4", "e5", "Nf3"], "possibleMoves": ["Nc3", "Bc4", "d4"], "color": "w"}
[/INST]
🎯 Output Format
Always a single-line JSON:
json
Copy
Edit
{"move": "Bc4", "reasoning": "Develops bishop and targets f7"}
The move must be from possibleMoves
The reasoning is free-form but short
🛠️ Training Details
Base: TinyLlama-1.1B-Chat
LoRA (8-bit): q_proj, k_proj, v_proj, o_proj
Epochs: 3
Dataset: ~70 samples from master-level PGNs
Format: instruction-style using transformers.Trainer
📈 Performance
| Metric | Value |
| ----------- | ----- |
| Final loss | 1.08 |
| Epochs | 3 |
| Batch size | 1 |
| Total steps | 51 |
🚀 Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Konvah/chess-tinyllama")
tokenizer = AutoTokenizer.from_pretrained("Konvah/chess-tinyllama")
prompt = """[INST]
You are a chess player.
{"moveHistory": ["e4", "e5", "Nf3"], "possibleMoves": ["Nc3", "Bc4", "d4"], "color": "w"}
[/INST]"""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
📎 License
Open for research and tournament evaluation. Not intended for production without additional safety testing.
✍️ Author
Ismail Abubakar (@boringcrypto_)
Contact: [email protected]
🏆 Aura Tournament
This model was created for the Aura Chess LLM Tournament to demonstrate reasoning and strategy prediction using open-source LLMs.
---
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