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
What is the current method to convert these models to work with Llama.cpp?
I have tried converting the model using some older methods floating around and it seems to not be converting them to the correct ggml format. What is the current methodology or scripts for this? Specifically regarding converting the .bin to .pth EDIT: Nevermind, I have it working in llama.cpp now. Great job!
I have tried converting the model using some older methods floating around and it seems to not be converting them to the correct ggml format. What is the current methodology or scripts for this? Specifically regarding converting the .bin to .pth EDIT: Nevermind, I have it working in llama.cpp now. Great job!
how??
Could a kind soul provide a magnet of the ggml quantitized binaries for llama.cpp?
Yeah, how ?
Just use convert.py script in the root of llama.cpp