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How to use Axolotl for LoRA

Q: How to use Axolotl for LoRA

Use Axolotl to apply LoRA fine-tuning by installing the package, preparing your base model and dataset, then running axolotl train with LoRA configuration. Axolotl simplifies LoRA training with built-in support for efficient parameter tuning and integration with Hugging Face models.

Quick answer

Use Axolotl to apply LoRA fine-tuning by installing the package, preparing your base model and dataset, then running axolotl train with LoRA configuration. Axolotl simplifies LoRA training with built-in support for efficient parameter tuning and integration with Hugging Face models.

PREREQUISITES

Python 3.8+
pip install axolotl
Basic knowledge of LoRA and Hugging Face Transformers
Access to a compatible pretrained model (e.g., LLaMA or GPT variants)

Setup

Install Axolotl via pip and prepare your environment variables if needed. Ensure you have a pretrained model checkpoint and dataset ready for fine-tuning.

bash

pip install axolotl

Step by step

Run LoRA fine-tuning using Axolotl's CLI or Python API. Below is a minimal example using the CLI to fine-tune a Hugging Face model with LoRA enabled.

bash

axolotl train \
  --model-name-or-path huggingface/llama-7b \
  --train-data path/to/dataset.jsonl \
  --output-dir ./lora-finetuned \
  --lora-rank 16 \
  --lora-alpha 32 \
  --lora-dropout 0.05 \
  --per-device-train-batch-size 8 \
  --num-train-epochs 3

output

Loading model huggingface/llama-7b...
Applying LoRA with rank=16, alpha=32, dropout=0.05
Starting training for 3 epochs...
Training complete. Model saved to ./lora-finetuned

Common variations

You can customize LoRA parameters like lora-rank, lora-alpha, and lora-dropout to balance performance and efficiency. Axolotl supports training with mixed precision and distributed setups. For Python API usage, import axolotl and configure LoRAConfig programmatically.

python

from axolotl import Trainer, LoRAConfig

lora_config = LoRAConfig(
    r=8,
    alpha=16,
    dropout=0.1
)

trainer = Trainer(
    model_name_or_path="huggingface/llama-7b",
    train_data_path="path/to/dataset.jsonl",
    output_dir="./lora-finetuned",
    lora_config=lora_config,
    per_device_train_batch_size=4,
    num_train_epochs=5
)

trainer.train()

output

Loading model huggingface/llama-7b...
Applying LoRA with rank=8, alpha=16, dropout=0.1
Starting training for 5 epochs...
Training complete. Model saved to ./lora-finetuned

Troubleshooting

If training fails due to out-of-memory errors, reduce per-device-train-batch-size or enable mixed precision.
Ensure your dataset is in the expected JSONL format with proper tokenization.
Verify your model checkpoint is compatible with LoRA fine-tuning.

✅

Key Takeaways

Install Axolotl with pip to enable easy LoRA fine-tuning on large models.
Use Axolotl CLI or Python API to configure LoRA parameters and run training.
Adjust batch size and LoRA hyperparameters to optimize memory and performance.
Prepare your dataset in JSONL format compatible with Axolotl's training pipeline.
Troubleshoot common issues by checking model compatibility and resource limits.

Verified 2026-04 · huggingface/llama-7b

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