Instructions to use zhoudoe23/ChessQween3-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zhoudoe23/ChessQween3-base with Transformers:

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

pipe = pipeline("text-generation", model="zhoudoe23/ChessQween3-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zhoudoe23/ChessQween3-base")
model = AutoModelForCausalLM.from_pretrained("zhoudoe23/ChessQween3-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zhoudoe23/ChessQween3-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zhoudoe23/ChessQween3-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zhoudoe23/ChessQween3-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zhoudoe23/ChessQween3-base

SGLang

How to use zhoudoe23/ChessQween3-base 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 "zhoudoe23/ChessQween3-base" \
    --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": "zhoudoe23/ChessQween3-base",
		"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 "zhoudoe23/ChessQween3-base" \
        --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": "zhoudoe23/ChessQween3-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zhoudoe23/ChessQween3-base with Docker Model Runner:
```
docker model run hf.co/zhoudoe23/ChessQween3-base
```

ChessQween3-base / training_args.bin

zhoudoe23

Training in progress, step 4000

0146236 verified 16 days ago

Download

History Blame Contribute Delete

5.27 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 37e0239ec4ede5ee114c304a5a2bd2cc1f382176f8adb6092e2233e4d357800e
Size of remote file:: 5.27 kB
SHA256:: 75c9df37dd9655c05d5d2fc7445e398122d7c70e7ff0597ba72902da4e1898e1

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.