Instructions to use Yossri23/chess-challenge-yossri-hdiji with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yossri23/chess-challenge-yossri-hdiji with Transformers:

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

pipe = pipeline("text-generation", model="Yossri23/chess-challenge-yossri-hdiji", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Yossri23/chess-challenge-yossri-hdiji", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Yossri23/chess-challenge-yossri-hdiji with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Yossri23/chess-challenge-yossri-hdiji"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yossri23/chess-challenge-yossri-hdiji",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Yossri23/chess-challenge-yossri-hdiji

SGLang

How to use Yossri23/chess-challenge-yossri-hdiji 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 "Yossri23/chess-challenge-yossri-hdiji" \
    --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": "Yossri23/chess-challenge-yossri-hdiji",
		"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 "Yossri23/chess-challenge-yossri-hdiji" \
        --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": "Yossri23/chess-challenge-yossri-hdiji",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Yossri23/chess-challenge-yossri-hdiji with Docker Model Runner:
```
docker model run hf.co/Yossri23/chess-challenge-yossri-hdiji
```

chess-challenge-yossri-hdiji / vocab.json

Yossri23

Chess Challenge submission by Yossri23

a0e7273 verified 7 months ago

Raw

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718 Bytes

{"[PAD]": 0, "[BOS]": 1, "[EOS]": 2, "[UNK]": 3, "a1": 4, "a2": 5, "a3": 6, "a4": 7, "a5": 8, "a6": 9, "a7": 10, "a8": 11, "b1": 12, "b2": 13, "b3": 14, "b4": 15, "b5": 16, "b6": 17, "b7": 18, "b8": 19, "c1": 20, "c2": 21, "c3": 22, "c4": 23, "c5": 24, "c6": 25, "c7": 26, "c8": 27, "d1": 28, "d2": 29, "d3": 30, "d4": 31, "d5": 32, "d6": 33, "d7": 34, "d8": 35, "e1": 36, "e2": 37, "e3": 38, "e4": 39, "e5": 40, "e6": 41, "e7": 42, "e8": 43, "f1": 44, "f2": 45, "f3": 46, "f4": 47, "f5": 48, "f6": 49, "f7": 50, "f8": 51, "g1": 52, "g2": 53, "g3": 54, "g4": 55, "g5": 56, "g6": 57, "g7": 58, "g8": 59, "h1": 60, "h2": 61, "h3": 62, "h4": 63, "h5": 64, "h6": 65, "h7": 66, "h8": 67, "q": 68, "r": 69, "b": 70, "n": 71}