Instructions to use zhoudoe23/ChessQween2-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zhoudoe23/ChessQween2-tiny with Transformers:

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

pipe = pipeline("text-generation", model="zhoudoe23/ChessQween2-tiny")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zhoudoe23/ChessQween2-tiny")
model = AutoModelForCausalLM.from_pretrained("zhoudoe23/ChessQween2-tiny", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zhoudoe23/ChessQween2-tiny with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zhoudoe23/ChessQween2-tiny"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zhoudoe23/ChessQween2-tiny",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zhoudoe23/ChessQween2-tiny

SGLang

How to use zhoudoe23/ChessQween2-tiny 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/ChessQween2-tiny" \
    --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/ChessQween2-tiny",
		"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/ChessQween2-tiny" \
        --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/ChessQween2-tiny",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zhoudoe23/ChessQween2-tiny with Docker Model Runner:
```
docker model run hf.co/zhoudoe23/ChessQween2-tiny
```

ChessQween2-tiny

File size: 2,011 Bytes

2eee2f6

---
library_name: transformers
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
model-index:
- name: ChessQween2-tiny
  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. -->

# ChessQween2-tiny

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: 2.3192

## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 3.9745        | 0.1009 | 101  | 3.9412          |
| 3.5969        | 0.2018 | 202  | 3.5234          |
| 3.2078        | 0.3028 | 303  | 3.1028          |
| 2.9051        | 0.4037 | 404  | 2.7821          |
| 2.7076        | 0.5046 | 505  | 2.5714          |
| 2.5810        | 0.6055 | 606  | 2.4494          |
| 2.5076        | 0.7065 | 707  | 2.3762          |
| 2.4721        | 0.8074 | 808  | 2.3372          |
| 2.4643        | 0.9083 | 909  | 2.3221          |
| 2.4602        | 1.0    | 1001 | 2.3192          |


### Framework versions

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