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Fine-tune LLaMA on consumer hardware with alpaca-lora and its LoRA training approach.
About alpaca-lora
alpaca-lora is an open-source repository with code to instruct-tune LLaMA models using low-rank adaptation (LoRA). It includes finetune.py for training, generate.py for inference with a Gradio interface, and export scripts that merge LoRA weights into the base model for Hugging Face format or PyTorch state_dicts. Training is described as running within hours on a single RTX 4090, and inference loads the base model from the Hugging Face hub with adapter weights from tloen/alpaca-lora-7b. The inference script is presented as example code to modify, and Dockerfile and docker-compose files are included for container use.
Key features
- Fine-tune LLaMA models with LoRA via finetune.py
- Run inference via Gradio using base model and LoRA weights
- Merge LoRA weights back into base model
- Export to Hugging Face format and PyTorch state_dicts
- Run training and inference inside Docker container
- Adjust training hyperparameters including LoRA rank and dropout
Best for: Researchers and developers who want to train and test Alpaca-style instruction tuning on LLaMA models.
Good for
- Includes training code, inference script, and published LoRA weights
- Training runs within hours on a single RTX 4090
- Includes Docker and Docker Compose setup for training and inference
Watch out for
- Inference script is example code users are expected to modify
- bitsandbytes may require installation from source if standard install fails
alpaca-lora at a glance
- Website
- github.com/tloen/alpaca-lora
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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alpaca-lora FAQ
What hardware is described for training?
The training code is described as running within hours on a single RTX 4090.
Where does generate.py get models for inference?
It reads the foundation model from the Hugging Face model hub and the LoRA weights from tloen/alpaca-lora-7b.
How can LoRA weights be used in other projects?
Use the export scripts to merge LoRA weights into the base model for Hugging Face format or PyTorch state_dicts, for use in projects like llama.cpp or alpaca.cpp.
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