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

Libraries

How to use zhoudoe23/ChessQween3-base-puzzled with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zhoudoe23/ChessQween3-base-puzzled with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zhoudoe23/ChessQween3-base-puzzled"
# 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-puzzled",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

ChessQween3-base-puzzled / README.md

zhoudoe23

End of training

aeb10f4 verified 15 days ago

preview code

Raw

History Blame Contribute Delete

3.81 kB

	---
	library_name: transformers
	base_model: zhoudoe23/ChessQween3-base
	tags:
	- generated_from_trainer
	model-index:
	- name: ChessQween3-base-puzzled
	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. -->

	# ChessQween3-base-puzzled

	This model is a fine-tuned version of [zhoudoe23/ChessQween3-base](https://huggingface.co/zhoudoe23/ChessQween3-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.8700

	## 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: 3e-05
	- 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 \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|
	\| 1.1666 \| 0.0233 \| 1000 \| 1.1704 \|
	\| 1.0454 \| 0.0466 \| 2000 \| 1.1195 \|
	\| 1.1005 \| 0.0700 \| 3000 \| 1.0880 \|
	\| 1.0877 \| 0.0933 \| 4000 \| 1.0663 \|
	\| 1.0387 \| 0.1166 \| 5000 \| 1.0488 \|
	\| 1.1939 \| 0.1399 \| 6000 \| 1.0355 \|
	\| 1.1149 \| 0.1633 \| 7000 \| 1.0199 \|
	\| 1.0303 \| 0.1866 \| 8000 \| 1.0077 \|
	\| 1.0135 \| 0.2099 \| 9000 \| 0.9969 \|
	\| 1.0281 \| 0.2332 \| 10000 \| 0.9883 \|
	\| 0.9677 \| 0.2566 \| 11000 \| 0.9776 \|
	\| 1.0151 \| 0.2799 \| 12000 \| 0.9704 \|
	\| 0.9752 \| 0.3032 \| 13000 \| 0.9628 \|
	\| 0.9267 \| 0.3265 \| 14000 \| 0.9540 \|
	\| 1.0725 \| 0.3499 \| 15000 \| 0.9486 \|
	\| 1.0237 \| 0.3732 \| 16000 \| 0.9456 \|
	\| 0.9124 \| 0.3965 \| 17000 \| 0.9355 \|
	\| 0.8746 \| 0.4198 \| 18000 \| 0.9291 \|
	\| 1.0226 \| 0.4431 \| 19000 \| 0.9235 \|
	\| 0.8150 \| 0.4665 \| 20000 \| 0.9190 \|
	\| 0.9453 \| 0.4898 \| 21000 \| 0.9152 \|
	\| 0.8654 \| 0.5131 \| 22000 \| 0.9089 \|
	\| 1.0067 \| 0.5364 \| 23000 \| 0.9057 \|
	\| 0.8130 \| 0.5598 \| 24000 \| 0.9016 \|
	\| 0.9912 \| 0.5831 \| 25000 \| 0.8974 \|
	\| 0.8276 \| 0.6064 \| 26000 \| 0.8950 \|
	\| 0.9481 \| 0.6297 \| 27000 \| 0.8912 \|
	\| 0.9450 \| 0.6531 \| 28000 \| 0.8884 \|
	\| 0.8170 \| 0.6764 \| 29000 \| 0.8853 \|
	\| 0.9074 \| 0.6997 \| 30000 \| 0.8833 \|
	\| 0.9123 \| 0.7230 \| 31000 \| 0.8817 \|
	\| 0.8216 \| 0.7464 \| 32000 \| 0.8783 \|
	\| 0.8683 \| 0.7697 \| 33000 \| 0.8765 \|
	\| 0.8481 \| 0.7930 \| 34000 \| 0.8748 \|
	\| 0.8536 \| 0.8163 \| 35000 \| 0.8735 \|
	\| 0.7900 \| 0.8397 \| 36000 \| 0.8729 \|
	\| 0.8798 \| 0.8630 \| 37000 \| 0.8718 \|
	\| 0.8715 \| 0.8863 \| 38000 \| 0.8712 \|
	\| 0.8726 \| 0.9096 \| 39000 \| 0.8705 \|
	\| 0.8342 \| 0.9329 \| 40000 \| 0.8702 \|
	\| 0.8996 \| 0.9563 \| 41000 \| 0.8700 \|
	\| 0.8560 \| 0.9796 \| 42000 \| 0.8700 \|


	### Framework versions

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