radu/LLamaRecipes @ c0405b6efc592d58f7fca438503f92d23463c77e

himanshushukla12 c0405b6efc Changes the UI from textbox to chatbox with max_tokens, rop_k, temperature and top_p sliders there.		před 11 měsíci
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RAG	24e8962953 Update hello_llama_cloud.ipynb (#584)	před 1 rokem
Running_Llama3_Anywhere	0f632b3e3d Fix version number in Python example	před 1 rokem
agents	cc569ef52b colab links fixed	před 1 rokem
finetuning	3f883f6ce2 added missing word and corrected spelling	před 11 měsíci
inference	c0405b6efc Changes the UI from textbox to chatbox with max_tokens, rop_k, temperature and top_p sliders there.	před 11 měsíci
Getting_to_know_Llama.ipynb	ee34e1be19 typo fix lama -> llama line 127	před 1 rokem
Prompt_Engineering_with_Llama_3.ipynb	cb05f6e01a Add files via upload	před 1 rokem
README.md	6addcb8fa0 move feature table to main readme	před 1 rokem

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:

README.md

Llama-Recipes Quickstart