Instructions to use Q-bert/ChessLlama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Q-bert/ChessLlama with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Q-bert/ChessLlama")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Q-bert/ChessLlama") model = AutoModelForCausalLM.from_pretrained("Q-bert/ChessLlama") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Q-bert/ChessLlama with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Q-bert/ChessLlama" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Q-bert/ChessLlama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Q-bert/ChessLlama
- SGLang
How to use Q-bert/ChessLlama 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 "Q-bert/ChessLlama" \ --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": "Q-bert/ChessLlama", "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 "Q-bert/ChessLlama" \ --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": "Q-bert/ChessLlama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Q-bert/ChessLlama with Docker Model Runner:
docker model run hf.co/Q-bert/ChessLlama
ChessLlama
Generated by DALL-E 3.
Model Details
This pre-trained model has been trained on the Llama architecture with the games of grand master chess players.
Model Description
- Developed by: Talha Rüzgar Akkuş
- Data Format: Universal Chess Interface (UCI)
- Model type: Llama Architecture
- License: apache-2.0
How to Get Started with the Model
This notebook is created to test the model's capabilities. You can use it to evaluate performance of the model.
Challenge
You can use this model or dataset to train your own models as well, and challenge me in this new field.
Training Details
Training Data
Training Procedure
This model was fully trained from scratch with random weights. It was created from the ground up with a new configuration and model, and trained using the Hugging Face Trainer for 1200 steps. There is still potential for further training. You can see the training code below.
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