Instructions to use maicomputer/alpaca-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maicomputer/alpaca-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="maicomputer/alpaca-13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("maicomputer/alpaca-13b") model = AutoModelForCausalLM.from_pretrained("maicomputer/alpaca-13b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use maicomputer/alpaca-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "maicomputer/alpaca-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "maicomputer/alpaca-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/maicomputer/alpaca-13b
- SGLang
How to use maicomputer/alpaca-13b 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 "maicomputer/alpaca-13b" \ --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": "maicomputer/alpaca-13b", "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 "maicomputer/alpaca-13b" \ --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": "maicomputer/alpaca-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use maicomputer/alpaca-13b with Docker Model Runner:
docker model run hf.co/maicomputer/alpaca-13b
The generation quality is really bad
#3
by RiverGao - opened
I have tried this model with the examples provided by Meta, which were released together with LLaMA, and the generation quality is simply unacceptable. For example, for some trials no token is generated apart from the input; Otherwise, it just generates some nonsense like "1999, 1999, 1999 ......"