Instructions to use LLM-course/chess-submission-v21-MDaytek with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-course/chess-submission-v21-MDaytek with Transformers:

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

pipe = pipeline("text-generation", model="LLM-course/chess-submission-v21-MDaytek", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("LLM-course/chess-submission-v21-MDaytek", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LLM-course/chess-submission-v21-MDaytek with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM-course/chess-submission-v21-MDaytek"
# 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-submission-v21-MDaytek",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LLM-course/chess-submission-v21-MDaytek

SGLang

How to use LLM-course/chess-submission-v21-MDaytek 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-submission-v21-MDaytek" \
    --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-submission-v21-MDaytek",
		"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-submission-v21-MDaytek" \
        --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-submission-v21-MDaytek",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LLM-course/chess-submission-v21-MDaytek with Docker Model Runner:
```
docker model run hf.co/LLM-course/chess-submission-v21-MDaytek
```

chess-submission-v21-MDaytek / README.md

MDaytek

Chess Challenge submission by MDaytek

7929e48 verified 4 months ago

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	---
	library_name: transformers
	tags:
	- chess
	- llm-course
	- chess-challenge
	license: mit
	---

	# chess-submission-v21-MDaytek

	Chess model submitted to the LLM Course Chess Challenge.

	## Submission Info

	- Submitted by: [MDaytek](https://huggingface.co/MDaytek)
	- Parameters: 1,143,744
	- Organization: LLM-course

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("LLM-course/chess-submission-v21-MDaytek", trust_remote_code=True)
	tokenizer = AutoTokenizer.from_pretrained("LLM-course/chess-submission-v21-MDaytek", trust_remote_code=True)
	```

	## Evaluation

	This model is evaluated at the [Chess Challenge Arena](https://huggingface.co/spaces/LLM-course/Chess1MChallenge).