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alpaca-lora

tloen/alpaca-lora

Instruct-tune LLaMA on consumer hardware

GitHub

GraphCanon updated 3w · GitHub synced 3w

19k stars2.2k forksLast push 2y Jupyter Notebook Apache-2.0

Decision brief

alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration.

Good fit when

When you have limited GPU resources but want to perform instruction-fine-tuning on the LLaMA model, and your setup supports basic Docker.
If you prefer working within a Jupyter Notebook environment to experiment and fine-tune language models using consumer-grade hardware.

Avoid when

When you require more advanced customization beyond what is offered through the `finetune.py` script parameters or Jupyter Notebook interface.
For teams with high-performance computing resources aiming for optimal performance, as alpaca-lora is optimized for use on consumer-grade hardware.

Pricing:: freemium - The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply.

Observed Jul 16, 2026 · Source: enrich:decision_facts

alpaca-lora alternatives Trust report

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Maintenance and security

Full trust report

Maintenance: Dormant (734d since push); As of 3w
Provenance: Not a fork · Personal account; As of 3w
Security (OSV): 1 critical, 5 high, 12 medium, 28 low (1 critical, 5 high, 12 medium, 28 low); As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

git clone https://github.com/tloen/alpaca-lora

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Repository for instruct tuning LLaMA model using consumer-grade hardware with options to build and run via Docker.

Capability facts

Deploy: Self-host
Source: dockerfile:Dockerfile · Aug 3, 2026
Docker: Dockerfile present
Source: dockerfile:Dockerfile · Aug 3, 2026
Languages: jupyter notebook, python
Source: github.language+pyproject.toml · Aug 3, 2026

README

Docker Setup & Inference

Build the container image:

docker build -t alpaca-lora .

Run the container (you can also use finetune.py and all of its parameters as shown above for training):

docker run --gpus=all --shm-size 64g -p 7860:7860 -v ${HOME}/.cache:/root/.cache --rm alpaca-lora generate.py \
    --load_8bit \
    --base_model 'decapoda-research/llama-7b-hf' \
    --lora_weights 'tloen/alpaca-lora-7b'

Open https://localhost:7860 in the browser

Docker Compose Setup & Inference

(optional) Change desired model and weights under environment in the docker-compose.yml
Build and run the container

docker-compose up -d --build

Open https://localhost:7860 in the browser
See logs:

docker-compose logs -f

Clean everything up:

docker-compose down --volumes --rmi all

For agents

This page has a .md twin and JSON over the API.

/tools/tloen-alpaca-lora.md GET /api/graphcanon/tools/tloen-alpaca-lora GET /api/graphcanon/graph?tool=tloen-alpaca-lora