Instructions to use wxjiao/alpaca-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wxjiao/alpaca-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="wxjiao/alpaca-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("wxjiao/alpaca-7b") model = AutoModelForCausalLM.from_pretrained("wxjiao/alpaca-7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use wxjiao/alpaca-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "wxjiao/alpaca-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "wxjiao/alpaca-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/wxjiao/alpaca-7b
- SGLang
How to use wxjiao/alpaca-7b 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 "wxjiao/alpaca-7b" \ --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": "wxjiao/alpaca-7b", "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 "wxjiao/alpaca-7b" \ --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": "wxjiao/alpaca-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use wxjiao/alpaca-7b with Docker Model Runner:
docker model run hf.co/wxjiao/alpaca-7b
This repo contains an in-house tuned LLaMA-7b based on the Stanford Alpaca dataset, for only research use.
Quantitative evaluation on machine translation and qualitative comparison on general abilities can be found at alpaca-mt.
| Translation Performance of LLMs on Flores Subsets. | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Direction | De-En | En-De | Zh-En | En-Zh | |||||||
| Metric | BLEU | COMET | BLEU | COMET | BLEU | COMET | BLEU | COMET | |||
| 45.04 | 0.8879 | 41.16 | 0.8861 | 31.66 | 0.8771 | 43.58 | 0.8842 | ||||
| DeepL | 49.23 | 0.8970 | 41.46 | 0.8903 | 31.22 | 0.8739 | 44.31 | 0.8811 | |||
| ChatGPT | 43.71 | 0.8910 | 38.87 | 0.8814 | 24.73 | 0.8581 | 38.27 | 0.8699 | |||
| GPT-4 | 46.00 | 0.8931 | 45.73 | 0.8928 | 28.50 | 0.8742 | 42.50 | 0.8840 | |||
| LLaMA-7b | 6.96 | 0.6548 | 3.64 | 0.5084 | 8.95 | 0.6340 | 0.10 | 0.4899 | |||
| Alpaca-7b | 36.00 | 0.8737 | 20.09 | 0.8003 | 14.37 | 0.8069 | 10.06 | 0.5604 | |||
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