Tools for LLM Tuning

Training large language models (LLMs) can feel like trying to tame a dragon: powerful, resource-hungry, and intimidating. But with the right tools, even beginners can achieve impressive results. In this guide, we’ll explore three popular frameworks—Unsloth, Axolotl, and MLX—that simplify LLM training, making it faster, cheaper, and more accessible. Let’s dive in!

Why Efficient Training Matters

LLMs like Llama 3, Mistral, and Gemma are powerful, but their default training processes often require expensive GPUs and weeks of time. Efficient training tools address these challenges by:

• Reducing memory usage (e.g., with 4-bit quantization).

• Speeding up training (optimized kernels and algorithms).

• Simplifying workflows (pre-built configurations and UI support).

Let’s see how these tools work in practice.

Tool 1: Unsloth — The Speed Demon

What Is Unsloth?

Unsloth is a lightweight library that accelerates LLM fine-tuning by 2–5x while reducing VRAM usage by up to 80%. It achieves this by rewriting PyTorch operations into Triton kernels (GPU-optimized code) without sacrificing accuracy.

Pros:

• Beginner-friendly: Pre-quantized models and example notebooks make setup easy.

• Single-GPU support: Works on older GPUs like T4 or RTX 3090 :cite[6].

• Zero accuracy loss: Exact math ensures no approximations :cite[7].

Cons:

• No multi-GPU support: Limited to single-GPU setups :cite[1].

• Model restrictions: Supports Llama, Mistral, Gemma, and Phi-3, but not all architectures :cite[4].

Code Example: Fine-Tuning Mistral-7B with Unsloth

from unsloth import FastLanguageModel

# Load a 4-bit quantized model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/mistral-7b-bnb-4bit",
    max_seq_length=2048,
    load_in_4bit=True,
)

# Attach LoRA adapters for efficient training
model = FastLanguageModel.get_peft_model(
    model,
    r=16, 
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
    lora_alpha=16,
    lora_dropout=0,
)

# Train with Hugging Face’s TRL
from trl import SFTTrainer

trainer = SFTTrainer(s
    model=model,
    dataset=your_dataset,
    args=TrainingArguments(
        per_device_train_batch_size=2,
        gradient_accumulation_steps=4,
        learning_rate=2e-5,
        output_dir="outputs",
    ),
)
trainer.train()

Tool 2: Axolotl

What Is Axolotl?

Axolotl is a YAML-driven framework that abstracts away the complexity of LLM training. It supports full fine-tuning, LoRA, QLoRA, and multi-GPU setups via DeepSpeed or FSDP.

Pros:

• Multi-GPU support: Scales across GPUs for large models 2.

• Flexible configurations: Customize via YAML files or CLI overrides 3.

• Broad model support: Works with Llama, Mistral, Falcon, and more 2.

Cons:

• Steeper learning curve: Requires YAML/CLI familiarity 3.

• Hardware demands: Full fine-tuning needs high-end GPUs 8.

base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
load_in_4bit: true
datasets:
  - path: /
    type: alpaca
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
per_device_train_batch_size: 2
gradient_accumulation_steps: 4
learning_rate: 3e-5
num_epochs: 3

Run training with:

axolotl train examples/llama-3/lora.yml

Tool 3: MLX

What Is MLX?

MLX is Apple’s framework for training LLMs on Apple Silicon (M1/M2/M3 chips). It leverages unified memory to reduce data transfers between CPU and GPU.

Pros:

• Apple Silicon optimization: Runs efficiently on MacBooks.

• Python-first API: Easy for Python developers.

• Research-friendly: Flexible for custom architectures.

Cons:

• Limited adoption: Fewer pre-trained models and tutorials.

• No Windows/Linux support: Exclusive to macOS.

import mlx.core as mx
from mlx.utils import tree_unflatten

# Load a model
model = mx.load_model("llama-3-8B-mlx")

# Fine-tune on a dataset
def train_step(model, inputs, labels):
    def loss_fn(params):
        logits = model(inputs, params)
        return mx.mean(mx.square(logits - labels))
    grad_fn = mx.value_and_grad(model, loss_fn)
    loss, grads = grad_fn(model.trainable_parameters())
    model.update(tree_unflatten(list(grads.items())))
    return loss

Tips for Efficient Training

1. Start small: Use QLoRA with rank 16–32 for quick experiments.

2. Leverage sample packing: Axolotl’s sample_packing: true reduces padding waste.

3. Monitor VRAM: Unsloth’s 4-bit models cut memory usage by 70%.

4. Use WandB logging: Track metrics for free with Axolotl’s integration.