Part 1: LLM Training Tools

Overview

Tools for fine-tuning, RLHF training, and adapting open-source LLMs for specific tasks.

Tool	Purpose	Best For	Complexity
Trl	Hugging Face's fine-tuning library	RLHF, supervised training	Medium
Axolotl	Flexible training framework	Multi-method, config-based	Medium
Unsloth	Ultra-fast LoRA training	Speed-critical projects	Low
Ludwig	No-code ML framework	Non-technical users	Low

Decision Tree

graph TD
    A["Need to train?"] -->|Standard fine-tuning| B["TRL or Axolotl"]
    A -->|Speed critical| C["Unsloth"]
    A -->|No coding| D["Ludwig"]
    B -->|Need RLHF| E["TRL (specialized)"]
    B -->|Want flexibility| F["Axolotl (config-based)"]

Quick Comparison

Memory Usage - TRL: 28GB (full model) to 4GB (QLoRA) - Axolotl: 4-28GB (flexible) - Unsloth: 10GB → 2GB (80% reduction) - Ludwig: 8GB (optimized)

Speed - TRL: 1x (baseline) - Axolotl: 1x (baseline) - Unsloth: 2-5x faster - Ludwig: 1-2x (optimized)

Ease of Use 1. Ludwig (no code) 2. Unsloth (simple API) 3. TRL (straightforward) 4. Axolotl (YAML config)

Cost (7B model, 10K examples) - TRL: $300-1200 (8×A100 to 1×RTX 4090) - Axolotl: $300-1200 - Unsloth: $50-200 (consumer GPU) - Ludwig: $200-800

Installation Quick Start

# TRL
pip install trl

# Axolotl
pip install axolotl

# Unsloth
pip install unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git

# Ludwig
pip install ludwig

Common Workflows

Workflow 1: Quick Fine-tuning (1-2 days)

→ Use Unsloth or Ludwig

Workflow 2: Production Model (1-2 weeks)

→ Use TRL or Axolotl with careful validation

Workflow 3: Research Experiments (ongoing)

→ Use Axolotl (flexibility) or TRL (RLHF support)

Workflow 4: Non-technical User

→ Use Ludwig (drag-and-drop style)

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Next: Pick a tool to learn more! Each has detailed examples and use cases.