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", device_map="auto") - 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
| library_name: transformers | |
| tags: [] | |
| <div align="center"> | |
| <img src="banner.png" alt="Banner" width="600"/> | |
| <div style="margin-top: 10px; display: flex; justify-content: center; gap: 20px;"> | |
| <a href="https://huggingface.co/spaces/mlabonne/chessllm" style="font-size: 18px; text-decoration: none;">⭐ Try Chennus ⭐</a> | |
| <a href="https://huggingface.co/DedeProGames" style="font-size: 18px; text-decoration: none;">👤 Profile</a> | |
| </div> | |
| </div> | |
| # Model Card for Chennus | |
| This is **Chennus**, my custom Chess AI model trained to play competitive chess on | |
| **[Chess LLM Arena](https://huggingface.co/spaces/mlabonne/chessllm)**. | |
| Chennus was trained on a **1500 ELO rating dataset**, and despite the modest training base, | |
| it achieved **high performance levels on | |
| [Chess LLM Arena](https://huggingface.co/spaces/mlabonne/chessllm)**. | |
| Chennus is **free for anyone to use for chess finetuning**, as long as you clearly state | |
| in your model card or template that your work was **based on Chennus**. | |