Instructions to use FlameF0X/ChessSLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FlameF0X/ChessSLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FlameF0X/ChessSLM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("FlameF0X/ChessSLM") model = AutoModelForCausalLM.from_pretrained("FlameF0X/ChessSLM") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use FlameF0X/ChessSLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FlameF0X/ChessSLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FlameF0X/ChessSLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/FlameF0X/ChessSLM
- SGLang
How to use FlameF0X/ChessSLM with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "FlameF0X/ChessSLM" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FlameF0X/ChessSLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "FlameF0X/ChessSLM" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FlameF0X/ChessSLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use FlameF0X/ChessSLM with Docker Model Runner:
docker model run hf.co/FlameF0X/ChessSLM
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README.md
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---
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license: apache-2.0
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datasets:
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library_name: transformers
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tags:
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pipeline_tag: text-generation
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---
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# ChessSLM
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ChessSLM can play chess by generating moves sequentially in SAN notation.
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It has been evaluated in matches against several language models, including:
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- Claude
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- Gemini
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- Qwen
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- GPT-2
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- GPT-Neo
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- Mistral
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- other small chess-oriented models
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The model achieves an
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| Model | Elo Rating |
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| EleutherAI/pythia-70m-deduped |
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| nlpguy/amdchess-v9 | 1094 |
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| nlpguy/smolchess-v2 | 1093 |
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| mlabonne/chesspythia-70m | 1088 |
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| **FlameF0X/ChessSLM** | **1087** |
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| DedeProGames/mini-chennus | 1083 |
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| distilbert/distilgpt2 | 1061 |
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| facebook/opt-125m | 1057 |
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| mlabonne/grandpythia-200k-70m | 1050 |
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| DedeProGames/Chesser-248K-Mini | 1048 |
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| bharathrajcl/chess_llama_68m | 1046 |
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---
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license: apache-2.0
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datasets:
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- mlabonne/chessllm
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library_name: transformers
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tags:
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- chess
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pipeline_tag: text-generation
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new_version: FlameF0X/ChessSLM-RL
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---
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# ChessSLM
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ChessSLM can play chess by generating moves sequentially in SAN notation.
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It has been evaluated in matches against several language models, including:
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- Claude [Won against it]
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- Gemini [Lost again it]
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- Qwen
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- GPT-2
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- GPT-Neo
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- Mistral
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- other small chess-oriented models
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The model achieves an averaging rating of **around ~1054 Elo** against other language models despite its small size.
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| Model | Elo Rating |
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| EleutherAI/pythia-70m-deduped | 1111 |
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| mlabonne/chesspythia-70m | 1101 |
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| nlpguy/amdchess-v9 | 1094 |
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| nlpguy/smolchess-v2 | 1093 |
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| DedeProGames/mini-chennus | 1083 |
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| distilbert/distilgpt2 | 1061 |
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| DedeProGames/dialochess | 1059 |
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| facebook/opt-125m | 1057 |
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| **FlameF0X/ChessSLM** | **1054** |
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| **FlameF0X/ChessSLM-RL** | **1054** |
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| mlabonne/grandpythia-200k-70m | 1050 |
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| DedeProGames/Chesser-248K-Mini | 1048 |
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