Update README.md

README.md

@@ -36,3 +36,62 @@ The model uses structured prompts:
 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.
+
+---
+