Short guides for Large Language Models.

Discover more resources/support here.

(I've put this in an order that might be useful for newcomers)

Basic:

• yourbench-space.md: Explains the options for running LLM fine-tuning or inference (generating responses) using a simple jupyter or Google Colab notebook.
• llm-notebook-setup.md: Explains the options for running LLM fine-tuning or inference (generating responses) using a simple jupyter or Google Colab notebook.
• Pushing_to_Hub.ipynb : all about downloading, uploading and pushing models (including adapters and quantized models) to HuggingFace Hub.
• Google_Colab_Llama_Simple_Inference.ipynb : A simple notebook to generate responses from a Llama 2 language model.

Intermediate:

• multi-gpu folder. This contains simple scripts to run training in model parallel, distributed data parallel and Fully Sharded Data Parallel.
• Understanding_Quantization_and_AWQ : Pairs with a YouTube video by TrelisResearch on AWQ quantization.
• 8_bit_quantization.ipynb : Use this notebook to push models to hub in 8-bit.
• LLM_Comparison.ipynb : Perform some basic comparisons of Language Model Performance
• llama-cpp-setup.md : Run an LLM on your laptop using llama.cpp
• jupyter-lab-setup : How to set up jupyter lab on your laptop

Once you've tried the basic scripts, consider trying:

• Embeddings
• Chat fine-tuning
• Fine-tuning for structured responses
• Supervised and Unsupervised fine-tuning
• Server Setup

One way to advance on these is via the Trelis YouTube Channel. You can also check out Trelis.com to purchase advanced fine-tuning and inference scripts.

• Add in basic scripts for running yourbench