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

Libraries

How to use sharpbai/alpaca-7b-merged with Transformers:

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

pipe = pipeline("text-generation", model="sharpbai/alpaca-7b-merged")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sharpbai/alpaca-7b-merged")
model = AutoModelForCausalLM.from_pretrained("sharpbai/alpaca-7b-merged")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sharpbai/alpaca-7b-merged with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sharpbai/alpaca-7b-merged

SGLang

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

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

Stanford Alpaca-7B-Merged

The weight file is split into chunks with a size of 405M for convenient and fast parallel downloads

This repo hosts the merged weight for Stanford Alpaca-7B that can be used directly. Below is the original model card information.

Stanford Alpaca-7B

This repo hosts the weight diff for Stanford Alpaca-7B that can be used to reconstruct the original model weights when applied to Meta's LLaMA weights.

To recover the original Alpaca-7B weights, follow these steps:

1. Convert Meta's released weights into huggingface format. Follow this guide:
    https://huggingface.co/docs/transformers/main/model_doc/llama
2. Make sure you cloned the released weight diff into your local machine. The weight diff is located at:
    https://huggingface.co/tatsu-lab/alpaca-7b/tree/main
3. Run this function with the correct paths. E.g.,
    python weight_diff.py recover --path_raw <path_to_step_1_dir> --path_diff <path_to_step_2_dir> --path_tuned <path_to_store_recovered_weights>

Once step 3 completes, you should have a directory with the recovered weights, from which you can load the model like the following

import transformers
alpaca_model = transformers.AutoModelForCausalLM.from_pretrained("<path_to_store_recovered_weights>")
alpaca_tokenizer = transformers.AutoTokenizer.from_pretrained("<path_to_store_recovered_weights>")

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