Instructions to use TheBloke/wizard-mega-13B-GGML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/wizard-mega-13B-GGML with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheBloke/wizard-mega-13B-GGML")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TheBloke/wizard-mega-13B-GGML", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use TheBloke/wizard-mega-13B-GGML with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheBloke/wizard-mega-13B-GGML" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheBloke/wizard-mega-13B-GGML", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TheBloke/wizard-mega-13B-GGML
- SGLang
How to use TheBloke/wizard-mega-13B-GGML 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 "TheBloke/wizard-mega-13B-GGML" \ --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": "TheBloke/wizard-mega-13B-GGML", "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 "TheBloke/wizard-mega-13B-GGML" \ --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": "TheBloke/wizard-mega-13B-GGML", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TheBloke/wizard-mega-13B-GGML with Docker Model Runner:
docker model run hf.co/TheBloke/wizard-mega-13B-GGML
How to install this locally and use offline?
How to install this locally and use offline? Any references or videos would be great
this is actually the GGML version (my bad) - you should be able to use it with LLama.cpp https://github.com/ggerganov/llama.cpp
Then you want llama-cpp-python, which are llama.cpp bindings for Python. Allows you to load GGML files exactly the same as llama.cpp does, but easily accessible from code.
It can then be used either direct from your own Python code, or via an OpenAI-compatible API which you can put LangChain at, or any other client.
https://github.com/abetlen/llama-cpp-python
https://pypi.org/project/llama-cpp-python/
you could use this: https://www.youtube.com/watch?v=aO5ZpFYHa0A&t=1s
Thanks everyone, goal accomplished