Instructions to use zhoudoe23/ChessQween-124m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zhoudoe23/ChessQween-124m with Transformers:

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

pipe = pipeline("text-generation", model="zhoudoe23/ChessQween-124m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zhoudoe23/ChessQween-124m")
model = AutoModelForCausalLM.from_pretrained("zhoudoe23/ChessQween-124m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zhoudoe23/ChessQween-124m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zhoudoe23/ChessQween-124m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zhoudoe23/ChessQween-124m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zhoudoe23/ChessQween-124m

SGLang

How to use zhoudoe23/ChessQween-124m 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/ChessQween-124m" \
    --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/ChessQween-124m",
		"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/ChessQween-124m" \
        --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/ChessQween-124m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zhoudoe23/ChessQween-124m with Docker Model Runner:
```
docker model run hf.co/zhoudoe23/ChessQween-124m
```

ChessQween-124m / README.md

zhoudoe23

End of training

e4ad53e verified 23 days ago

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	---
	library_name: transformers
	license: mit
	base_model: openai-community/gpt2
	tags:
	- generated_from_trainer
	model-index:
	- name: ChessQween-124m
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# ChessQween-124m

	This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.1389

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 0.00015
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 42
	- gradient_accumulation_steps: 8
	- total_train_batch_size: 256
	- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_steps: 200
	- num_epochs: 1
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|
	\| 4.0290 \| 0.1008 \| 78 \| 3.8540 \|
	\| 1.8302 \| 0.2017 \| 156 \| 1.7799 \|
	\| 1.5722 \| 0.3025 \| 234 \| 1.5030 \|
	\| 1.4223 \| 0.4034 \| 312 \| 1.3733 \|
	\| 1.3197 \| 0.5042 \| 390 \| 1.2666 \|
	\| 1.2458 \| 0.6050 \| 468 \| 1.2069 \|
	\| 1.2211 \| 0.7059 \| 546 \| 1.1701 \|
	\| 1.1914 \| 0.8067 \| 624 \| 1.1490 \|
	\| 1.1668 \| 0.9076 \| 702 \| 1.1399 \|
	\| 1.1770 \| 1.0 \| 774 \| 1.1389 \|


	### Framework versions

	- Transformers 5.14.1
	- Pytorch 2.10.0+cu128
	- Datasets 5.0.0
	- Tokenizers 0.22.2