Instructions to use Yossri23/chess_yossri_hdiji_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yossri23/chess_yossri_hdiji_2 with Transformers:

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

pipe = pipeline("text-generation", model="Yossri23/chess_yossri_hdiji_2", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Yossri23/chess_yossri_hdiji_2", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Yossri23/chess_yossri_hdiji_2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Yossri23/chess_yossri_hdiji_2"
# 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_yossri_hdiji_2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Yossri23/chess_yossri_hdiji_2

SGLang

How to use Yossri23/chess_yossri_hdiji_2 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_yossri_hdiji_2" \
    --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_yossri_hdiji_2",
		"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_yossri_hdiji_2" \
        --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_yossri_hdiji_2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Yossri23/chess_yossri_hdiji_2 with Docker Model Runner:
```
docker model run hf.co/Yossri23/chess_yossri_hdiji_2
```

chess_yossri_hdiji_2

File size: 496 Bytes

669cac0

{
  "architectures": [
    "ChessForCausalLM"
  ],
  "bos_token_id": 1,
  "dropout": 0.1,
  "dtype": "float32",
  "eos_token_id": 2,
  "layer_norm_epsilon": 1e-05,
  "model_type": "chess_transformer",
  "n_ctx": 256,
  "n_embd": 128,
  "n_head": 4,
  "n_inner": 448,
  "n_layer": 5,
  "pad_token_id": 0,
  "tie_weights": true,
  "transformers_version": "4.57.1",
  "vocab_size": 84,
  "auto_map": {
    "AutoConfig": "model.ChessConfig",
    "AutoModelForCausalLM": "model.ChessForCausalLM"
  }
}