Instructions to use LLM-course/chess-etiennelefranc-colab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-course/chess-etiennelefranc-colab with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LLM-course/chess-etiennelefranc-colab")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("LLM-course/chess-etiennelefranc-colab", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LLM-course/chess-etiennelefranc-colab with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM-course/chess-etiennelefranc-colab"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM-course/chess-etiennelefranc-colab",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LLM-course/chess-etiennelefranc-colab

SGLang

How to use LLM-course/chess-etiennelefranc-colab 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 "LLM-course/chess-etiennelefranc-colab" \
    --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": "LLM-course/chess-etiennelefranc-colab",
		"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 "LLM-course/chess-etiennelefranc-colab" \
        --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": "LLM-course/chess-etiennelefranc-colab",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LLM-course/chess-etiennelefranc-colab with Docker Model Runner:
```
docker model run hf.co/LLM-course/chess-etiennelefranc-colab
```

chess-etiennelefranc-colab

Modèle de chess entraîné sur Google Colab pour le Chess Challenge.

Informations de soumission

Soumis par: etienneLefranc
Paramètres: 909,824
Organisation: LLM-course
Entraîné sur: Google Colab GPU

Détails du modèle

Architecture: Chess Transformer (GPT-style)
Taille du vocabulaire: 1682
Dimension d'embedding: 128
Nombre de couches: 4
Nombre de têtes d'attention: 4
Epochs d'entraînement: 3
Samples d'entraînement: 50,000

Downloads last month: -

Safetensors

Model size

910k params

Tensor type

F32