Instructions to use MightyDragon-Dev/Chinese-Dragon-GPT1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MightyDragon-Dev/Chinese-Dragon-GPT1 with Transformers:

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

pipe = pipeline("text-generation", model="MightyDragon-Dev/Chinese-Dragon-GPT1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MightyDragon-Dev/Chinese-Dragon-GPT1")
model = AutoModelForCausalLM.from_pretrained("MightyDragon-Dev/Chinese-Dragon-GPT1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MightyDragon-Dev/Chinese-Dragon-GPT1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MightyDragon-Dev/Chinese-Dragon-GPT1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MightyDragon-Dev/Chinese-Dragon-GPT1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MightyDragon-Dev/Chinese-Dragon-GPT1

SGLang

How to use MightyDragon-Dev/Chinese-Dragon-GPT1 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 "MightyDragon-Dev/Chinese-Dragon-GPT1" \
    --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": "MightyDragon-Dev/Chinese-Dragon-GPT1",
		"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 "MightyDragon-Dev/Chinese-Dragon-GPT1" \
        --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": "MightyDragon-Dev/Chinese-Dragon-GPT1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MightyDragon-Dev/Chinese-Dragon-GPT1 with Docker Model Runner:
```
docker model run hf.co/MightyDragon-Dev/Chinese-Dragon-GPT1
```

Chinese-Dragon-GPT1 🐉

Chinese-Dragon-GPT1 is a lightweight, 82.7-million parameter Transformer model trained from scratch on Chinese news data. It follows the GPT-1 (Post-LayerNorm) architecture and was optimized for efficient training and inference on consumer-grade hardware.

Model Details

Architecture: GPT-1 (Custom Small Configuration)
Parameters: 82,659,840
Layers (n_layer): 6
Hidden Dimension (n_embd): 768
Attention Heads (n_head): 12
Context Length (n_positions): 256
Vocabulary Size: 52,000 (Custom Byte-Level BPE)
Training Dataset: CLUE TNews (Chinese News headlines)

Training Story ⚙️

This model was trained as part of the MightyDragon-Dev project. Unlike most LLMs trained on massive GPU clusters, this model was trained on an Intel i5-10210U (ThinkPad X13 Laptop) using CPU-only optimization.

Training Time: ~15 hours
Total Steps: 12,500
Final Training Loss: ~0.13
Hardware: 4 Cores @ 2.2GHz Boost / 16GB RAM

How to Use

You can use this model directly with the Hugging Face pipeline API:

from transformers import pipeline

generator = pipeline(
    "text-generation", 
    model="MightyDragon-Dev/Chinese-Dragon-GPT1"
)

# Example Prompt
prompt = "据报道，"
result = generator(prompt, max_length=50, do_sample=True, top_k=50)
print(result[0]['generated_text'])

Downloads last month: 37

Safetensors

Model size

82.7M params

Tensor type

F32

MightyDragon-Dev
/

Chinese-Dragon-GPT1

Chinese-Dragon-GPT1 🐉

Model Details

Training Story ⚙️

How to Use

Dataset used to train MightyDragon-Dev/Chinese-Dragon-GPT1