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LLM Distributed Finetune

LLM Distributed Finetune is a Python repository of scripts and configuration for fine-tuning Hugging Face language models across multiple GPUs. It uses Ray AIR to orchestrate an AWS cluster and DeepSpeed for parameter and optimizer sharding and offloading.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

LLM Distributed Finetune — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a code-based starting point for distributed Hugging Face fine-tuning on AWS and can configure credentials and manage the Ray cluster.

Who it is for

  • Developers setting up distributed fine-tuning for Hugging Face language models.
  • Teams using AWS GPU workers that want a Ray-managed cluster workflow.
  • Practitioners who need to adjust DeepSpeed settings or run jobs without DeepSpeed.

Documented uses

  • Submit a Hugging Face model fine-tuning job to multiple GPU workers.
  • Provision, connect to, and shut down an AWS-backed Ray cluster.
  • Configure DeepSpeed behavior or disable it for a job.
  • Use the documented Alpaca or Alpaca Spanish datasets in fine-tuning workflows.
Before you choose

Requirements & limits

The public GitHub repository is open source under the MIT license.

  • The documented cluster workflow targets AWS and requires AWS credentials configured with awscli.
  • Job submission is through the repository’s Python script and command-line arguments.
  • DeepSpeed can be configured in config/deepspeed.json or disabled with --no-deepspeed.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and submit a distributed fine-tuning job

Clone the repository, provision its AWS-backed Ray cluster, and submit the README’s example FALCON-7B job.

Before you start

  • AWS credentials configured with awscli.
  • Ray and the repository’s other runtime requirements available in the working environment.
  • An Alpaca instruction-data file for the documented example.

Steps

  1. Clone the repository with the command below.

    Documented command
    git clone https://github.com/AdrianBZG/LLM-distributed-finetune

  2. Configure AWS credentials with aws configure.

  3. Start the cluster with ray up ray_cluster.yaml, then connect using ray attach ray_cluster.yaml.

  4. Run the command below.

    Documented command
    python finetune.py --model="tiiuae/falcon-7b" --num-workers 4 --data alpaca_data_cleaned.json

Expected outcome

Submits a FALCON-7B fine-tuning job using four GPU workers and the specified Alpaca data file.

Guide limitations

  • The example assumes the named data file is available to the job.
  • The README does not specify AWS instance types, costs, or a full dependency-installation procedure.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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17 October 2026
Original website title
AdrianBZG/LLM-distributed-finetune · Official homepage title · retained in its source language
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