Explain Axolotl with examples to fine tune Models

Axolotl is becoming popular in the fine-tuning space, especially for LLMs (Large Language Models).

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🦎 What is Axolotl?

Axolotl is an open-source framework designed to make fine-tuning large language models (LLMs) simple, efficient, and flexible.
It's built to handle LoRA (Low-Rank Adaptation) and other parameter-efficient fine-tuning techniques with ease.

It is inspired by libraries like Hugging Face's Transformers, but adds convenient tools specifically for customizing, training, and managing LLM fine-tuning.

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🔑 Key Features of Axolotl:

1. Supports various fine-tuning methods:

   • Full fine-tuning.
   • LoRA (most commonly used!)
   • QLoRA (Quantized LoRA).

2. Supports multiple model families:

   • LLaMA, Mistral, Falcon, GPT-NeoX, etc.

3. Highly configurable with YAML files.

4. Integrated with Hugging Face, so you can load pre-trained models easily.

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🚀 Fine-tuning Example Workflow:

Objective:

Fine-tune a pre-trained LLaMA-2-7B model on a custom dataset of Q&A pairs using LoRA.

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1️⃣ Install Axolotl

pip install axolotl

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2️⃣ Prepare Data (JSONL Format)

Example dataset.jsonl:

{"instruction": "What is the capital of France?", "input": "", "output": "Paris"}
{"instruction": "Who wrote Hamlet?", "input": "", "output": "William Shakespeare"}

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3️⃣ Create Config File (YAML)

Example config.yaml:

base_model: 'meta-llama/Llama-2-7b-hf'
dataset:
  path: './dataset.jsonl'
  format: 'alpaca'  # Supports various formats, alpaca-style Q&A is common
lora:
  r: 8
  alpha: 16
  dropout: 0.05
  target_modules: 'all-linear'
training:
  epochs: 3
  batch_size: 8
  learning_rate: 2e-4
output:
  output_dir: './llama2-finetuned'

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4️⃣ Run Fine-tuning

axolotl train config.yaml

✅ That’s it! It handles:

• Loading the base model.
• Applying LoRA adapters.
• Preparing the dataset.
• Training loop & saving the output.

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5️⃣ Inference Using Fine-tuned Model:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('./llama2-finetuned')
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-hf')

input_text = "What is the capital of France?"
inputs = tokenizer(input_text, return_tensors='pt')
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))

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🌟 Advantages of Axolotl:

• Simplifies fine-tuning for large models.
• No need to write complex training scripts.
• Easy LoRA integration to save GPU memory.
• Supports advanced techniques like QLoRA, 4-bit quantization, etc.
• YAML-based configs make experiments reproducible.

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🔥 Use Cases:

Use Case	How Axolotl Helps
Fine-tuning chatbots	Quickly fine-tune Llama or Mistral on Q&A datasets.
Domain adaptation	Train models on specific industry datasets (e.g., legal, medical).
Language-specific models	Fine-tune multilingual models for a specific language.
Low-resource environments	Use LoRA/QLoRA to fine-tune big models with limited hardware.

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Here's a ready-to-run Axolotl fine-tuning setup tailored for you.

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🎯 Objective:

Fine-tune a pre-trained LLaMA-2-7B model using LoRA on a custom Q&A dataset.

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🟢 Folder Structure:

project/
├── dataset.jsonl        # Your Q&A dataset
├── config.yaml          # Axolotl config
└── run.sh               # Script to start training

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1️⃣ Prepare Dataset (dataset.jsonl):

Each line is a JSON object:

{"instruction": "What is the capital of France?", "input": "", "output": "Paris"}
{"instruction": "Who developed Python?", "input": "", "output": "Guido van Rossum"}

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2️⃣ Create Config File (config.yaml):

base_model: 'meta-llama/Llama-2-7b-hf'
dataset:
  path: './dataset.jsonl'
  format: 'alpaca'
lora:
  r: 8
  alpha: 16
  dropout: 0.05
  target_modules: 'all-linear'
training:
  epochs: 3
  batch_size: 4
  learning_rate: 2e-4
  save_every_n_steps: 500
output:
  output_dir: './llama2-finetuned'
tokenizer:
  truncation_side: 'left'
  padding_side: 'right'
  max_length: 2048

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3️⃣ Create Training Script (run.sh):

#!/bin/bash

# Install dependencies
pip install axolotl transformers datasets

# Start training
axolotl train config.yaml

Make it executable:

chmod +x run.sh

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4️⃣ Run Fine-tuning:

./run.sh

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5️⃣ Inference After Fine-tuning:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('./llama2-finetuned')
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-hf')

input_text = "Who developed Python?"
inputs = tokenizer(input_text, return_tensors='pt')
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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🚀 Quick Customization:

• Different Model:
  Change base_model in config.yaml to any supported Hugging Face model, e.g., mistralai/Mistral-7B-v0.1.

• Dataset Format:
  Supports alpaca, vicuna, oasst, etc.
  You can adjust based on dataset style.

• LoRA Parameters:
  Tune r, alpha, dropout depending on memory budget.

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Hardware Requirements:

• Minimum: 1 GPU (24GB VRAM recommended for 7B models with LoRA)
• For QLoRA: You can even run with lower VRAM (12-16GB) using quantization!

Summary:

Axolotl = Lightweight, configurable, and efficient framework to fine-tune large language models (LLMs), especially with LoRA/QLoRA.

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