Instructions to use DedeProGames/chennus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DedeProGames/chennus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DedeProGames/chennus")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DedeProGames/chennus") model = AutoModelForCausalLM.from_pretrained("DedeProGames/chennus") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DedeProGames/chennus with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DedeProGames/chennus" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DedeProGames/chennus", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DedeProGames/chennus
- SGLang
How to use DedeProGames/chennus 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 "DedeProGames/chennus" \ --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": "DedeProGames/chennus", "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 "DedeProGames/chennus" \ --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": "DedeProGames/chennus", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DedeProGames/chennus with Docker Model Runner:
docker model run hf.co/DedeProGames/chennus
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### [⭐ Try Chennus ⭐](https://huggingface.co/spaces/mlabonne/chessllm)
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This is **Chennus**, my custom Chess AI model trained to play competitive chess on
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**[Chess LLM Arena](https://huggingface.co/spaces/mlabonne/chessllm)**.
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[Chess LLM Arena](https://huggingface.co/spaces/mlabonne/chessllm)**.
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Chennus is **free for anyone to use for chess finetuning**, as long as you clearly state
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in your model card or template that your work was **based on Chennus**.
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tags: []
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<img src="banner.png" alt="Banner" width="600"/>
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<div style="margin-top: 10px; display: flex; justify-content: center; gap: 20px;">
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<a href="https://huggingface.co/spaces/mlabonne/chessllm" style="font-size: 18px; text-decoration: none;">⭐ Try Chennus ⭐</a>
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<a href="https://huggingface.co/DedeProGames" style="font-size: 18px; text-decoration: none;">👤 Profile</a>
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</div>
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# Model Card for Chennus
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This is **Chennus**, my custom Chess AI model trained to play competitive chess on
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**[Chess LLM Arena](https://huggingface.co/spaces/mlabonne/chessllm)**.
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[Chess LLM Arena](https://huggingface.co/spaces/mlabonne/chessllm)**.
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Chennus is **free for anyone to use for chess finetuning**, as long as you clearly state
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in your model card or template that your work was **based on Chennus**.
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