jperiodlangley
commented
3 weeks ago Outline provided by perplexity.ai
I'm giving a talk this week about llama-cpp-python. What outline should I use?
Here is a suggested outline for your talk on llama-cpp-python:
Introduction
- Brief overview of llama.cpp and its purpose
- Importance of running large language models locally
Installation
- Requirements: Python 3.8+, C compiler
- Installing llama-cpp-python via pip
- Installing with CUDA, Metal, BLAS support[1][2]
Loading the Model
- Downloading model files from Hugging Face
- Initializing the model using llama-cpp-python[1]
- Setting context length and other parameters
Using the API
- High-level Python API for text completion
- OpenAI-like API
- LangChain and LlamaIndex compatibility[2][3]
Performance Considerations
- Importance of using the correct number of threads[4]
- Comparing CPU vs GPU performance
- Quantization and its impact on speed[1]
Examples and Use Cases
- Running inference on sample prompts
- Integrating llama-cpp-python with other libraries
- Deploying models in standalone apps[5]
Conclusion
- Recap of llama-cpp-python's key features
- Future developments and roadmap
- Resources for further learning
The outline covers the key aspects of llama-cpp-python - installation, model loading, API usage, performance tuning, and real-world examples. Feel free to adjust the depth based on your target audience and available time. The search results provide good supporting information for each section.
Citations:
[1] Llama.cpp - Outlines 〰️ https://outlines-dev.github.io/outlines/reference/models/llamacpp/
[2] Python bindings for llama.cpp - GitHub https://github.com/abetlen/llama-cpp-python
[3] How to Run LLaMA Locally on CPU or GPU - YouTube https://www.youtube.com/watch?v=SvjWDX2NqiM
[4] Poor performance - How do I use LlamCpp in python correctly ... https://github.com/ggerganov/llama.cpp/discussions/6322
[5] Poll: Why do you choose llama.cpp over vLLM or vice-versa? - Reddit https://www.reddit.com/r/LocalLLaMA/comments/1bub23v/poll_why_do_you_choose_llamacpp_over_vllm_or/
Description
Working on this now. Looking at how to incorporate llama-cpp-python into the RAG application for SpaceApps. It provides a docker image with server conforming to OpenAI API, so should be easy to connect with and use.
https://github.com/abetlen/llama-cpp-python
Complete the following items to get a presentation ready for Huntsville AI
Adding material to the presentations repository
Add the file to present (prefer Jupyter Notebooks or Markdown formated files) to the folder structure. For multiple files, create a directory following the naming convention and add the files to it.
Naming convention
We use a convention of starting the filenames with a date (year/month/day) so that the files are still sorted by date even when in alphabetical format.
YYMMDD_Session_Description.extension