Instructions to use umd-zhou-lab/claude2-alpaca-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use umd-zhou-lab/claude2-alpaca-7B with Transformers:

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

pipe = pipeline("text-generation", model="umd-zhou-lab/claude2-alpaca-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("umd-zhou-lab/claude2-alpaca-7B")
model = AutoModelForCausalLM.from_pretrained("umd-zhou-lab/claude2-alpaca-7B", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use umd-zhou-lab/claude2-alpaca-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "umd-zhou-lab/claude2-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": "umd-zhou-lab/claude2-alpaca-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/umd-zhou-lab/claude2-alpaca-7B

SGLang

How to use umd-zhou-lab/claude2-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 "umd-zhou-lab/claude2-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": "umd-zhou-lab/claude2-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 "umd-zhou-lab/claude2-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": "umd-zhou-lab/claude2-alpaca-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use umd-zhou-lab/claude2-alpaca-7B with Docker Model Runner:
```
docker model run hf.co/umd-zhou-lab/claude2-alpaca-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for umd-zhou-lab/claude2-alpaca-7B

This model is trained by fine-tuning llama-2 with claude2 alpaca data.

Model Details

Model Description

Developed by: UMD Tianyi Zhou Lab
Model type: An auto-regressive language model based on the transformer architecture
License: Llama 2 Community License Agreement
Finetuned from model: meta-llama/Llama-2-7b

Model Sources

GitHub: Claude2-Alpaca
Data: claude2_alpaca

Uses

The primary use of this model is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.

Training

We use the prompt from Stanford Alpaca

Hyperparameter	Global Batch Size	Learning rate	Epochs	Max length	Weight decay
Model (7B)	128	2e-5	3	4096	0

Performance

Compared to the llama2-chat, our models can have better average performance.

	Average	ARC	HellaSwag	MMLU	TruthfulQA	Alpaca_Eval	Avg Length
Llama-2-7b-chat	56.335	52.9	78.55	48.32	45.57	71.37	1479
Llama-2-13b-chat	59.935	59.04	81.94	54.64	44.12	81.09	1513

claude_alpaca-7b	57.78	56.66	81.17	46.58	46.71	71.23	1066
claude_alpaca-13b	61.29	61.18	84.08	55.74	44.18	78.93	1127

Citation

Please consider citing our paper if you think our codes, data, or models are useful. Thank you!

@misc{claude2-alpaca,
  author = {Lichang Chen and Khalid Saifullah and Ming Li and Tianyi Zhou and Heng Huang},
  title = {Claude2-Alpaca: Instruction tuning datasets distilled from claude},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Lichang-Chen/claude2-alpaca}},
}

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