Monireh Ebrahimi
50fcb53165
removed unnecessary images and updated colab link
|
vor 11 Monaten | |
|---|---|---|
| .. | ||
| NotebookLlama | vor 11 Monaten | |
| RAG | vor 1 Jahr | |
| Running_Llama3_Anywhere | vor 1 Jahr | |
| agents | vor 1 Jahr | |
| finetuning | vor 11 Monaten | |
| images | vor 11 Monaten | |
| inference | vor 1 Jahr | |
| Getting_to_know_Llama.ipynb | vor 1 Jahr | |
| Prompt_Engineering_with_Llama_3.ipynb | vor 1 Jahr | |
| README.md | vor 1 Jahr | |
| build_with_Llama_3_2.ipynb | vor 11 Monaten | |
README.md
Llama-Recipes Quickstart
If you are new to developing with Meta Llama models, this is where you should start. This folder contains introductory-level notebooks across different techniques relating to Meta Llama.
- The Running_Llama_Anywhere notebooks demonstrate how to run Llama inference across Linux, Mac and Windows platforms using the appropriate tooling.
- The Prompt_Engineering_with_Llama notebook showcases the various ways to elicit appropriate outputs from Llama. Take this notebook for a spin to get a feel for how Llama responds to different inputs and generation parameters.
- The inference folder contains scripts to deploy Llama for inference on server and mobile. See also 3p_integrations/vllm and 3p_integrations/tgi for hosting Llama on open-source model servers.
- The RAG folder contains a simple Retrieval-Augmented Generation application using Llama.
- The finetuning folder contains resources to help you finetune Llama on your custom datasets, for both single- and multi-GPU setups. The scripts use the native llama-recipes finetuning code found in finetuning.py which supports these features: