Chess Gemma 3 fine-tuned model with commentary generation

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,300 @@
+# Chess Gemma Commentary 🎯♟️
+### By NAKST Studio
+<br>
+Fine-tuned **Gemma 3 270M** model for generating chess move commentary, ELO predictions, and move classifications.
+
+## Model Details
+
+- **Base Model:** Google Gemma 3 270M (270 Million Parameters)
+- **Fine-tuning Method:** LoRA (Low-Rank Adaptation) - Rank 8, Alpha 16
+- **Training Data:** 17,900+ chess positions with expert commentary
+- **Training Epochs:** 3
+- **Training Framework:** Unsloth + Hugging Face Transformers
+- **Hardware:** Google Colab T4 GPU
+- **Model Size:** 500MB (full) / 150MB (quantized q4_k_m)
+
+## Capabilities
+
+✅ **Chess Move Commentary** - Detailed analysis of chess positions and moves  
+✅ **ELO Prediction** - Estimates player skill rating (1000-2800)  
+✅ **Move Classification** - Labels moves as Best Move, Good Move, Blunder, etc.  
+✅ **Mobile Ready** - Works on Android with flutter_gemma or Ollama  
+✅ **Offline** - No internet required for inference  
+
+## Input Format
+
+The model expects chess position data formatted EXACTLY as follows:
+
+```
+Analyze this chess move:
+    FEN: rnbqkbnr/pppppppp/8/8/3P4/8/PPP1PPPP/RNBQKBNR b KQkq - 0 1,
+    SAN: Nf6,
+    Player Color: Black,
+    Move Classification: Book Move,
+    Best Alternative Move: g8f6,
+    CP Before: 27,
+    CP After: 21,
+    Opening: Queen's Pawn Game,
+    Name: Player_123,
+    is Player Or Bot: Player
+Provide Commentary, predicted elo, classification.
+```
+
+### Field Descriptions (In Order)
+
+| Field | Type | Required | Example                                                                                                      | Explanation |
+|-------|------|----------|--------------------------------------------------------------------------------------------------------------|-------------|
+| **FEN** | string | ✅ REQUIRED | `rnbqkbnr/pppppppp/8/8/3P4/8/PPP1PPPP/RNBQKBNR b KQkq - 0 1`                                                 | Forsyth-Edwards Notation - exact chess position before the move. This is the standard notation that describes where every piece is on the board. |
+| **SAN** | string | ✅ REQUIRED | `Nf6`                                                                                                        | Standard Algebraic Notation - the move that was played. Examples: e4, Nxf6, O-O (castling), Qh5+, exd5 |
+| **Player Color** | string | ✅ REQUIRED | `Black` or `White`                                                                                           | Which side played the move. Must be exactly "White" or "Black" |
+| **Move Classification** | string | ✅ REQUIRED | `Book Move`, `Best Move`, `Good Move`, `Inaccuracy`, `Blunder`, `Brilliant`, `Great`, `Inaccuracy`, `Mistake` | Category of the move. Common values: "Book Move", "Best Move", "Good Move", "Inaccuracy", "Blunder", "Forced Move" |
+| **Best Alternative Move** | string | ✅ REQUIRED | `g8f6`                                                                                                       | What the engine recommends instead (in coordinate notation). Example: if move is Nf6, alternative might be d6, e6, etc. |
+| **CP Before** | integer | ✅ REQUIRED | `27`                                                                                                         | Centipawn evaluation BEFORE the move. Positive = White better, Negative = Black better. 100 cp ≈ 1 pawn |
+| **CP After** | integer | ✅ REQUIRED | `21`                                                                                                         | Centipawn evaluation AFTER the move. Shows the impact of the move on the position |
+| **Opening** | string | ⭐ OPTIONAL | `Queen's Pawn Game`                                                                                          | Opening name from opening database. Can be "None" if unknown |
+| **Name** | string | ⭐ OPTIONAL | `Player_123`                                                                                                 | Player name or ID. Can be "Unknown" or "..." if not applicable |
+| **is Player Or Bot** | string | ✅ REQUIRED | `Player`, `Bot`, `Not Sure`                                                                                  | Whether the move was made by a human player or chess engine. Must be one of these three exact values |
+
+## Sample Input & Output
+
+### Example 1: Strong Opening
+
+**Input:**
+```
+Analyze this chess move:
+    FEN: rnbqkbnr/pppppppp/8/8/3P4/8/PPP1PPPP/RNBQKBNR b KQkq - 0 1,
+    SAN: Nf6,
+    Player Color: Black,
+    Move Classification: Book Move,
+    Best Alternative Move: g8f6,
+    CP Before: 27,
+    CP After: 21,
+    Opening: Queen's Pawn Game,
+    Name: Player_8007,
+    is Player Or Bot: Player
+Provide Commentary, predicted elo, classification.
+```
+
+**Output:**
+```
+Commentary: Excellent opening response! Nf6 is the classical development move against d4. It immediately challenges the center and prepares flexible piece development. This maintains the tension and gives Black excellent counterplay.
+
+Predicted ELO: 1850
+
+Classification: Book Move
+```
+
+### Example 2: Middlegame Move
+
+**Input:**
+```
+Analyze this chess move:
+    FEN: r1bqkb1r/pppp1ppp/2n2n2/4p3/4P3/5N2/PPPP1PPP/RNBQKB1R w KQkq e6 0 4,
+    SAN: Nc3,
+    Player Color: White,
+    Move Classification: Best Move,
+    Best Alternative Move: b1c3,
+    CP Before: 42,
+    CP After: 38,
+    Opening: Nimzo-Indian Defense,
+    Name: GrandMaster_Smith,
+    is Player Or Bot: Player
+Provide Commentary, predicted elo, classification.
+```
+
+**Output:**
+```
+Commentary: Strong centralization! Nc3 develops with pressure on e4 and controls key central squares. This move prepares to complete development while maintaining the center tension. A solid, principled continuation in this dynamic position.
+
+Predicted ELO: 2400
+
+Classification: Best Move
+```
+
+## Usage Examples
+
+### Python (Transformers)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("your-username/chess-gemma-commentary")
+tokenizer = AutoTokenizer.from_pretrained("your-username/chess-gemma-commentary")
+
+prompt = """Analyze this chess move:
+    FEN: rnbqkbnr/pppppppp/8/8/3P4/8/PPP1PPPP/RNBQKBNR b KQkq - 0 1,
+    SAN: Nf6,
+    Player Color: Black,
+    Move Classification: Book Move,
+    Best Alternative Move: g8f6,
+    CP Before: 27,
+    CP After: 21,
+    Opening: Queen's Pawn Game,
+    Name: Player_123,
+    is Player Or Bot: Player
+Provide Commentary, predicted elo, classification."""
+
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Flutter (flutter_gemma)
+```dart
+import 'package:flutter_gemma/flutter_gemma.dart';
+
+class ChessAnalyzer {
+  late GemmaModel model;
+
+  Future<void> initModel() async {
+    model = await GemmaModel.load(
+      modelPath: 'assets/model.safetensors',
+      tokenizerPath: 'assets/tokenizer.model',
+      configPath: 'assets/config.json',
+    );
+  }
+
+  Future<String> analyzeMove({
+    required String fen,
+    required String san,
+    required String playerColor,
+    required String moveClassification,
+    required String bestAltMove,
+    required int cpBefore,
+    required int cpAfter,
+    String opening = 'None',
+    String name = 'Unknown',
+    required String isPlayerOrBot,
+  }) async {
+    final prompt = """Analyze this chess move:
+    FEN: $fen,
+    SAN: $san,
+    Player Color: $playerColor,
+    Move Classification: $moveClassification,
+    Best Alternative Move: $bestAltMove,
+    CP Before: $cpBefore,
+    CP After: $cpAfter,
+    Opening: $opening,
+    Name: $name,
+    is Player Or Bot: $isPlayerOrBot
+Provide Commentary, predicted elo, classification.""";
+
+    return await model.generate(prompt: prompt, maxTokens: 256);
+  }
+}
+
+// Usage
+final analyzer = ChessAnalyzer();
+await analyzer.initModel();
+
+final result = await analyzer.analyzeMove(
+  fen: 'rnbqkbnr/pppppppp/8/8/3P4/8/PPP1PPPP/RNBQKBNR b KQkq - 0 1',
+  san: 'Nf6',
+  playerColor: 'Black',
+  moveClassification: 'Book Move',
+  bestAltMove: 'g8f6',
+  cpBefore: 27,
+  cpAfter: 21,
+  opening: 'Queen\'s Pawn Game',
+  name: 'Player_123',
+  isPlayerOrBot: 'Player',
+);
+
+print(result);
+```
+
+## Output Format
+
+The model generates three key components:
+
+1. **Commentary:** Multi-sentence chess analysis (5-50 words typically)
+2. **Predicted ELO:** Integer rating (1000-2800 typically)
+3. **Classification:** Single label describing the move
+
+## Performance Metrics
+
+- ⚡ **Inference Speed:** 10-20 tokens/second on mid-range Android phones
+- 💾 **Memory Required:** 4GB minimum RAM for on-device inference
+- 📱 **Model Sizes:** 
+  - Full precision: 500MB
+  - Quantized (q4_k_m): 150MB
+- 🎯 **Pattern Accuracy:** ~92% consistency with training data
+
+## Training Configuration
+
+- **LoRA Rank (r):** 8
+- **LoRA Alpha:** 16
+- **LoRA Dropout:** 0.1
+- **Target Modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Learning Rate:** 2e-4
+- **Batch Size:** 8 (effective; per device: 1, gradient accumulation: 8)
+- **Optimizer:** AdamW 8-bit
+- **Warmup Steps:** 50
+- **Training Time:** ~40 minutes (3 epochs on Colab T4)
+
+## Model Files
+
+```
+chess-gemma-commentary/
+├── model.safetensors          # Fine-tuned weights (500MB)
+├── tokenizer.model            # SentencePiece tokenizer
+├── tokenizer.json             # Tokenizer config
+├── tokenizer_config.json      # Tokenizer settings
+├── config.json                # Model architecture config
+├── chat_template.jinja        # Chat formatting template
+├── added_tokens.json          # Special tokens
+└── README.md                  # Documentation
+```
+
+## Important Notes
+
+⚠️ **Format Sensitivity:** This model is trained on the EXACT format shown above. Follow field order, spacing, and punctuation precisely for best results.
+
+⚠️ **Commas Matter:** Notice commas after each field (except the last one). Don't remove them.
+
+✅ **Optional Fields:** Only "Opening" and "Name" are optional - all others are required.
+
+✅ **Flexible Values:** You can change the values, but keep the field labels and format identical.
+
+✅ **Multi-position:** Works well for opening, middlegame, and endgame positions.
+
+## Known Limitations
+
+- ❌ Very unusual or impossible positions may generate generic responses
+- ❌ Requires 4GB+ RAM for mobile inference (quantization helps)
+- ❌ Temperature affects output randomness (0.7 recommended for chess)
+- ❌ Cannot analyze positions with invalid FEN notation
+
+## License
+
+This model is distributed under the **Gemma Community License**. See: https://ai.google.dev/gemma/terms
+
+## Citation
+
+```bibtex
+@model{chess_gemma_commentary_2025,
+  title={Chess Gemma Commentary},
+  author={Your Name},
+  year={2025},
+  howpublished={Hugging Face Hub}
+}
+```
+
+## Credits
+
+- **Base Model:** Google Gemma 3 (https://ai.google.dev/gemma)
+- **Fine-tuning:** Unsloth (https://unsloth.ai)
+- **Training Hardware:** Google Colab Free GPU
+- **Inspiration:** Chess.com & Lichess communities
+
+## Support & Feedback
+
+- 🐛 **Found a bug?** Open an issue on the model page
+- 💡 **Feature request?** Leave a discussion comment
+- ⭐ **Enjoying it?** Star the model!
+- 💙 **Our Site** https://nakststudio.com/
+
+---
+
+**Made with ❤️ by NAKST Studio**
+
+*Last Updated: November 3, 2025*

added_tokens.json

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+{
+  "<image_soft_token>": 262144
+}

chat_template.jinja

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+{{ bos_token }}
+{%- if messages[0]['role'] == 'system' -%}
+    {%- if messages[0]['content'] is string -%}
+        {%- set first_user_prefix = messages[0]['content'] + '
+
+' -%}
+    {%- else -%}
+        {%- set first_user_prefix = messages[0]['content'][0]['text'] + '
+
+' -%}
+    {%- endif -%}
+    {%- set loop_messages = messages[1:] -%}
+{%- else -%}
+    {%- set first_user_prefix = "" -%}
+    {%- set loop_messages = messages -%}
+{%- endif -%}
+{%- for message in loop_messages -%}
+    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
+        {{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
+    {%- endif -%}
+    {%- if (message['role'] == 'assistant') -%}
+        {%- set role = "model" -%}
+    {%- else -%}
+        {%- set role = message['role'] -%}
+    {%- endif -%}
+    {{ '<start_of_turn>' + role + '
+' + (first_user_prefix if loop.first else "") }}
+    {%- if message['content'] is string -%}
+        {{ message['content'] | trim }}
+    {%- elif message['content'] is iterable -%}
+        {%- for item in message['content'] -%}
+            {%- if item['type'] == 'image' -%}
+                {{ '<start_of_image>' }}
+            {%- elif item['type'] == 'text' -%}
+                {{ item['text'] | trim }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{ raise_exception("Invalid content type") }}
+    {%- endif -%}
+    {{ '<end_of_turn>
+' }}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{ '<start_of_turn>model
+' }}
+{%- endif -%}

config.json

@@ -0,0 +1,56 @@
+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "torch_dtype": "float16",
+  "eos_token_id": 106,
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 640,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "layer_types": [
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 18,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000.0,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 512,
+  "transformers_version": "4.56.2",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.10.12",
+  "use_bidirectional_attention": false,
+  "use_cache": true,
+  "vocab_size": 262144
+}

model.safetensors

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special_tokens_map.json

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+{
+  "boi_token": "<start_of_image>",
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eoi_token": "<end_of_image>",
+  "eos_token": {
+    "content": "<end_of_turn>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "image_token": "<image_soft_token>",
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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tokenizer.model

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