Instructions to use NAKSTStudio/chess-gemma-commentary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use NAKSTStudio/chess-gemma-commentary with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NAKSTStudio/chess-gemma-commentary:F16
# Run inference directly in the terminal:
llama cli -hf NAKSTStudio/chess-gemma-commentary:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NAKSTStudio/chess-gemma-commentary:F16
# Run inference directly in the terminal:
llama cli -hf NAKSTStudio/chess-gemma-commentary:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf NAKSTStudio/chess-gemma-commentary:F16
# Run inference directly in the terminal:
./llama-cli -hf NAKSTStudio/chess-gemma-commentary:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf NAKSTStudio/chess-gemma-commentary:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NAKSTStudio/chess-gemma-commentary:F16

Use Docker

docker model run hf.co/NAKSTStudio/chess-gemma-commentary:F16

LM Studio
Jan
Ollama
How to use NAKSTStudio/chess-gemma-commentary with Ollama:
```
ollama run hf.co/NAKSTStudio/chess-gemma-commentary:F16
```

Unsloth Studio

How to use NAKSTStudio/chess-gemma-commentary with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for NAKSTStudio/chess-gemma-commentary to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for NAKSTStudio/chess-gemma-commentary to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NAKSTStudio/chess-gemma-commentary to start chatting

Docker Model Runner
How to use NAKSTStudio/chess-gemma-commentary with Docker Model Runner:
```
docker model run hf.co/NAKSTStudio/chess-gemma-commentary:F16
```

Lemonade

How to use NAKSTStudio/chess-gemma-commentary with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NAKSTStudio/chess-gemma-commentary:F16

Run and chat with the model

lemonade run user.chess-gemma-commentary-F16

List all available models

lemonade list

Atomic Chat

NAKSTStudio commited on Nov 9, 2025

Commit

607d836

verified ·

1 Parent(s): 30bea28

Chess Gemma 3 fine-tuned model with commentary generation

Browse files

Files changed (3) hide show

.gitattributes +1 -0
README.md +3 -3
chess-commentary-model.task +3 -0

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@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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 *.zst filter=lfs diff=lfs merge=lfs -text
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README.md CHANGED Viewed

@@ -30,7 +30,7 @@ language:
 # Chess Gemma Commentary 🎯♟️
 ### By NAKST Studio
 <br>
-Fine-tuned <strong>Gemma 3 270M</strong> model for generating chess move commentary, ELO predictions, and move classifications in <strong>14 languages</strong>.
 ---
@@ -51,11 +51,11 @@ Fine-tuned <strong>Gemma 3 270M</strong> model for generating chess move comment
 - **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)
 - **Languages Supported:** 14 (English, Hindi, Spanish, Mandarin Chinese, French, German, Portuguese, Russian, Japanese, Arabic, Korean, Turkish, Indonesian, Bengali)
 ## Capabilities

 # Chess Gemma Commentary 🎯♟️
 ### By NAKST Studio
 <br>
+Fine-tuned <strong>Gemma 3 270M</strong> model for generating chess move commentary, ELO predictions, and move classifications in <strong>14 languages</strong>. Includes an optional .task file for lightweight mobile inference with flutter_gemma
 ---
 - **Base Model:** Google Gemma 3 270M (270 Million Parameters)
 - **Fine-tuning Method:** LoRA (Low-Rank Adaptation) - Rank 8, Alpha 16
+- **Training Data:** 25,000+ chess positions with expert commentary
 - **Training Epochs:** 3
 - **Training Framework:** Unsloth + Hugging Face Transformers
 - **Hardware:** Google Colab T4 GPU
+- **Model Size:** 500MB (full) / 270 mb .task (int 8 dynamic quantized)
 - **Languages Supported:** 14 (English, Hindi, Spanish, Mandarin Chinese, French, German, Portuguese, Russian, Japanese, Arabic, Korean, Turkish, Indonesian, Bengali)
 ## Capabilities

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+size 284368243