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

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.9453

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: 0.0003
train_batch_size: 128
eval_batch_size: 64
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 2
total_train_batch_size: 512
total_eval_batch_size: 128
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: 1000
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
3.7947	0.0256	2000	3.7973
3.2355	0.0512	4000	3.3104
3.1324	0.0768	6000	3.0861
3.0801	0.1025	8000	2.9574
2.9370	0.1281	10000	2.8806
2.8139	0.1537	12000	2.7924
2.7011	0.1793	14000	2.7364
2.5453	0.2049	16000	2.6827
2.6728	0.2305	18000	2.6320
2.5903	0.2561	20000	2.5825
2.5910	0.2817	22000	2.5478
2.4977	0.3074	24000	2.5110
2.5118	0.3330	26000	2.4747
2.4336	0.3586	28000	2.4338
2.4289	0.3842	30000	2.3929
2.3808	0.4098	32000	2.3621
2.3663	0.4354	34000	2.3340
2.3412	0.4610	36000	2.3026
2.3884	0.4866	38000	2.2735
2.3094	0.5123	40000	2.2408
2.3330	0.5379	42000	2.2155
2.1700	0.5635	44000	2.1895
2.1146	0.5891	46000	2.1646
2.1595	0.6147	48000	2.1398
2.0966	0.6403	50000	2.1140
2.2037	0.6659	52000	2.0877
1.9864	0.6915	54000	2.0719
2.0506	0.7172	56000	2.0551
1.9694	0.7428	58000	2.0328
2.1410	0.7684	60000	2.0160
2.0000	0.7940	62000	1.9990
1.9383	0.8196	64000	1.9859
2.0483	0.8452	66000	1.9706
1.9896	0.8708	68000	1.9616
1.9138	0.8965	70000	1.9552
2.0671	0.9221	72000	1.9515
1.9800	0.9477	74000	1.9467
1.9927	0.9733	76000	1.9455
1.9911	0.9989	78000	1.9453

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

Downloads last month: 769

Safetensors

Model size

0.1B params

Tensor type

F32

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ChessQween3-base

ChessQween3-base

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for zhoudoe23/ChessQween3-base

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