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--- |
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language: en |
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license: gemma |
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tags: |
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- lora |
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- peft |
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- adapters |
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- unsloth |
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- gemma-3 |
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- chess |
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- instruction-tuning |
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- sft |
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- trl |
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- 270m |
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base_model: unsloth/gemma-3-270m-it |
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library_name: transformers |
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pipeline_tag: text-generation |
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datasets: |
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- Thytu/ChessInstruct |
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model-index: |
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- name: gemma3-270m-chess-lora |
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results: [] |
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--- |
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# Gemma-3 270M — Chess Coach (LoRA Adapters) |
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**Author:** [@codertrish](https://huggingface.co/codertrish) |
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**Base model:** [`unsloth/gemma-3-270m-it`](https://huggingface.co/unsloth/gemma-3-270m-it) |
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**Type:** **LoRA adapters** (attach to base at load-time) |
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**Task:** Conversational chess tutoring (rules, openings, beginner tactics) |
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This repo contains **only the LoRA adapter weights** (ΔW). You must also load the **base model** and then **attach** these adapters to reproduce the fine-tuned behavior. |
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--- |
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## ✨ Intended Use |
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- **Direct use:** Teach or explain beginner chess concepts, opening principles, and simple tactics in plain English. |
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- **Downstream use:** As a lightweight add-on for apps where distributing full weights isn’t desired or allowed. |
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**Out-of-scope:** Engine-grade move calculation or authoritative evaluations of complex positions. For strong analysis, pair with a chess engine (e.g., Stockfish). |
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--- |
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## 🔧 How to Use (attach adapters) |
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### Option A — Unsloth (simplest) |
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```python |
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# pip install "unsloth[torch]" transformers peft accelerate bitsandbytes sentencepiece |
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from unsloth import FastModel |
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from unsloth.chat_templates import get_chat_template |
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BASE = "unsloth/gemma-3-270m-it" # base checkpoint |
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ADAPTER= "codertrish/gemma3-270m-chess-lora" # this repo |
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model, tok = FastModel.from_pretrained( |
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BASE, max_seq_length=2048, load_in_4bit=True, full_finetuning=False |
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) |
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tok = get_chat_template(tok, "gemma3") # Gemma-3 chat formatting |
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model.load_adapter(ADAPTER) # <-- attach LoRA |
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messages = [ |
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{"role":"system","content":"You are a helpful chess coach. Answer in plain text."}, |
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{"role":"user","content":"List 3 opening principles for beginners."}, |
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] |
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prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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out = model.generate(**tok([prompt], return_tensors="pt").to(model.device), |
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max_new_tokens=200, do_sample=False) |
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print(tok.decode(out[0], skip_special_tokens=True)) |
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