LoRA Toolkit#

Configure and generate LoRA fine-tuning scripts for large language models. Supports Llama, Mistral, Qwen, Phi and other HuggingFace-compatible models.

Commands#

config#

Generate a LoRA training configuration for your model and hardware.

bash

bash scripts/script.sh config --model llama3-8b --gpu 24gb --dataset 10000

Parameters:

• --model — base model (llama3-8b, mistral-7b, qwen2-7b, phi3-mini, llama3-70b)
• --gpu — VRAM size (8gb, 16gb, 24gb, 40gb, 80gb)
• --dataset — number of training samples

estimate#

Estimate VRAM usage, training time, and cost before starting.

bash

bash scripts/script.sh estimate --model mistral-7b --gpu 16gb --dataset 5000 --epochs 3

generate#

Generate a ready-to-run Python training script using HuggingFace PEFT + TRL.

bash

bash scripts/script.sh generate --model llama3-8b --output train.py

validate#

Check dataset format compatibility (Alpaca / ShareGPT / OpenAI Chat format).

bash

bash scripts/script.sh validate --file dataset.json --format alpaca

recommend#

Recommend the best base model for your use case and hardware.

bash

bash scripts/script.sh recommend --task chat --gpu 16gb --language en

help#

Show all commands.

bash

bash scripts/script.sh help

LoRA Parameters Reference#

Model SizeRecommended RankAlphaVRAM (4-bit)7B16–3232–648–12 GB13B163214–18 GB70B8–1616–3240–48 GB

Supported Dataset Formats#

• Alpaca: {"instruction": "...", "input": "...", "output": "..."}
• ShareGPT: {"conversations": [{"from": "human", "value": "..."}, ...]}
• OpenAI Chat: {"messages": [{"role": "user", "content": "..."}, ...]}

Requirements#

• Python 3.8+
• Optional: pip install transformers peft trl datasets for script execution

Feedback#

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