Instructions to use lazy-guy12/chess-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lazy-guy12/chess-llama with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lazy-guy12/chess-llama")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lazy-guy12/chess-llama") model = AutoModelForCausalLM.from_pretrained("lazy-guy12/chess-llama") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use lazy-guy12/chess-llama with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lazy-guy12/chess-llama" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lazy-guy12/chess-llama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lazy-guy12/chess-llama
- SGLang
How to use lazy-guy12/chess-llama 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 "lazy-guy12/chess-llama" \ --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": "lazy-guy12/chess-llama", "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 "lazy-guy12/chess-llama" \ --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": "lazy-guy12/chess-llama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lazy-guy12/chess-llama with Docker Model Runner:
docker model run hf.co/lazy-guy12/chess-llama
Is the Python training code available?
#5 opened 2 months ago
by
LeMoussel
Conversion to GGUF?
#4 opened 10 months ago
by
davidpfarrell
Brilliant model
4
#2 opened 10 months ago
by
1littlecoder
AttributeError: 'NoneType' object has no attribute 'decode'
#1 opened over 1 year ago
by
llamameta