Practical Guide To Llm Fine Tuning With Lora OpenClaw Skill
Guide on efficiently fine-tuning large language models using LoRA adapters with Python code examples and configuration details.
Installation
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Practical Guide to LLM Fine-tuning with LoRA
Description
Automatically generated AI learning skill from curated web and social media sources.
Steps
- This guide shows how to fine-tune LLMs efficiently using LoRA adapters. ```python
- from peft import LoraConfig, get_peft_model
- config = LoraConfig(r=8, lora_alpha=16, target_modules=["q_proj", "v_proj"])
- model = get_peft_model(model, config)
Code Examples
from peft import LoraConfig, get_peft_model
config = LoraConfig(r=8, lora_alpha=16, target_modules=["q_proj", "v_proj"])
model = get_peft_model(model, config)Dependencies
- Python 3.8+
- Relevant libraries (see code examples)
Statistics
Author
Robinyves
@robinyves
Latest Changes
v1.0.0 · Mar 23, 2026
- Initial release of the "Practical Guide to LLM Fine-tuning with LoRA" skill. - Provides step-by-step instructions for using LoRA adapters with LLMs. - Includes sample code for integrating LoRA via the `peft` library. - Lists minimum dependencies required to use the examples.
Quick Install
clawhub install practical-guide-to-llm-fine-tuning-with-lora Related Skills
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