Instructions to use keyfan/chinese-alpaca-7b-gptq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use keyfan/chinese-alpaca-7b-gptq with Transformers:

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

pipe = pipeline("text-generation", model="keyfan/chinese-alpaca-7b-gptq")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("keyfan/chinese-alpaca-7b-gptq")
model = AutoModelForCausalLM.from_pretrained("keyfan/chinese-alpaca-7b-gptq")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use keyfan/chinese-alpaca-7b-gptq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "keyfan/chinese-alpaca-7b-gptq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keyfan/chinese-alpaca-7b-gptq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/keyfan/chinese-alpaca-7b-gptq

SGLang

How to use keyfan/chinese-alpaca-7b-gptq 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 "keyfan/chinese-alpaca-7b-gptq" \
    --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": "keyfan/chinese-alpaca-7b-gptq",
		"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 "keyfan/chinese-alpaca-7b-gptq" \
        --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": "keyfan/chinese-alpaca-7b-gptq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use keyfan/chinese-alpaca-7b-gptq with Docker Model Runner:
```
docker model run hf.co/keyfan/chinese-alpaca-7b-gptq
```

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Check out the documentation for more information.

Chinese-Alpaca-7B-GPTQ

Chinese-Alpaca-7B-GPTQ is based on the Chinese-LLaMA-Alpaca model, and was quantized using GPTQ for faster inference with reduced memory requirements.

We used bigscience-data/roots_zh-cn_wikipedia for calibration.

Usage

To use Chinese-Alpaca-7B-GPTQ, you will need to use the GPTQ-for-LLaMa repository to load the model.

python llama_inference.py ./chinese-alpaca-7b-gptq --wbits 4 --groupsize 128 --load chinese-alpaca-7b-gptq/llama7b-4bit-128g.pt --text "### Instruction: 为什么苹果支付 没有在中国流行？\n\n### Response:"

Acknowledgments

We would like to thank the original authors of above-mentioned projects for their contributions to the NLP community.

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Usage

Acknowledgments

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