Auto-Ollama & Auto-GGUF ⚡️

Inference or Quantize Large Language Models (LLMs) Locally with a Single Command

Overview

Auto-Ollama is a toolkit designed to simplify the inference or Quantization of Large Language Models (LLMs) directly on your local environment. With an emphasis on ease of use and flexibility, Auto-Ollama supports both direct usage and conversion of models into an efficient format for local deployment.

For quantization, check out the new package called Auto-QuantLLM ⚡️. It's currently under development, but it aims to provide a streamlined and user-friendly approach to quantizing large language models (LLMs) with different Quantization methods.

Getting Started

Installation

Clone the repository to get started with Auto-Ollama:

git clone https://github.com/monk1337/auto-ollama.git
cd auto-ollama

Quick Tour

Running Auto-Ollama Use the autollama.sh script to quickly inference LLMs. This script requires the model name and the quantized file name as arguments.

# Deploy Large Language Models (LLMs) locally with Auto-Ollama
# Usage:
# ./scripts/autollama.sh -m <model path> -g <gguf file name>


# Example command:
./scripts/autollama.sh -m TheBloke/MistralLite-7B-GGUF -g mistrallite.Q4_K_M.gguf

Handling Non-Quantized Models with AutoGGUF

If your desired model is not available in a quantized format suitable for local deployment, Auto-Ollama offers the AutoGGUF utility. This tool can convert any Hugging Face model into the GGUF format and upload it to the Hugging Face model hub.

# Convert your Hugging Face model to GGUF format for local deployment
# Usage:
# ./scripts/autogguf.sh -m <MODEL_ID> [-u USERNAME] [-t TOKEN] [-q QUANTIZATION_METHODS]

# Example command:
./scripts/autogguf.sh -m unsloth/gemma-2b

More Options

# if want to upload the gguf model to hub after the conversion, provide the user and token
# Example command:
./scripts/autogguf.sh -m unsloth/gemma-2b -u user_name -t hf_token


#if wants to provide QUANTIZATION_METHODS
# Example command:
./scripts/autogguf.sh -m unsloth/gemma-2b -u user_name -t hf_token -q "q4_k_m,q5_k_m"

Quantization Recommendations

• Use Q5_K_M for the best performance-resource balance.
• Q4_K_M is a good choice if you need to save memory.
• K_M versions generally perform better than K_S.

Support and Contributions

For issues, suggestions, or contributions, please open an issue or pull request in the GitHub repository. We welcome contributions from the community to make Auto-Ollama even better!