Fine-tuning Tools Comparison

Choose the right fine-tuning framework for training LLMs on Clore.ai GPU servers.

Fine-tuning adapts a pre-trained LLM to your specific task or domain. This guide compares the four leading open-source tools: Unsloth, Axolotl, LLaMA-Factory, and TRL — covering speed, memory efficiency, supported models, and ease of use.

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Quick Decision Matrix

	Unsloth	Axolotl	LLaMA-Factory	TRL
Best for	Speed + memory	Config-driven training	Beginner-friendly	Research + RLHF
Speed vs baseline	2-5× faster	~1× (standard)	~1× (standard)	~1× (standard)
Memory reduction	70-80% less	QLoRA standard	QLoRA standard	Standard
RLHF/DPO/PPO	Basic	✅	✅	✅ (native)
WebUI	❌	❌	✅	❌
GitHub stars	23K+	9K+	37K+	10K+
License	LGPL (free for non-commercial)	Apache 2.0	Apache 2.0	Apache 2.0

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Overview

Unsloth

Unsloth is laser-focused on one thing: making fine-tuning as fast and memory-efficient as possible. It rewrites key operations in Triton and optimizes CUDA kernels.

Philosophy: Maximum speed, minimum VRAM — no compromises.

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Llama-3.2-8B-Instruct",
    max_seq_length=2048,
    load_in_4bit=True,  # 4-bit quantization
)

model = FastLanguageModel.get_peft_model(
    model,
    r=16,              # LoRA rank
    target_modules=["q_proj", "k_proj", "v_proj", "up_proj", "down_proj"],
    lora_alpha=16,
    lora_dropout=0,
    bias="none",
    use_gradient_checkpointing="unsloth",  # ~30% more batch size
    random_state=42,
)

Axolotl

Axolotl wraps HuggingFace Transformers with a YAML-based configuration system. It handles the complexity of training setup so you can focus on data and hyperparameters.

Philosophy: Everything in YAML, full flexibility underneath.

# config.yml
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

datasets:
  - path: mhenrichsen/alpaca_data_cleaned
    type: alpaca

load_in_4bit: true
adapter: qlora

lora_r: 32
lora_alpha: 16
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj

num_epochs: 3
micro_batch_size: 2
gradient_accumulation_steps: 4
learning_rate: 2e-4

LLaMA-Factory

LLaMA-Factory supports the widest range of models (100+) and training methods, with a web UI for configuration. It's the most accessible option for non-researchers.

Philosophy: Everything works, for everyone.

# Train via command line
llamafactory-cli train \
  --model_name_or_path meta-llama/Meta-Llama-3-8B \
  --stage sft \
  --do_train \
  --dataset alpaca_gpt4_en \
  --template llama3 \
  --finetuning_type lora \
  --lora_rank 8 \
  --output_dir saves/llama3-8b-lora \
  --num_train_epochs 3.0 \
  --per_device_train_batch_size 2

# Or use WebUI
llamafactory-cli webui

TRL (Transformer Reinforcement Learning)

TRL is HuggingFace's official RLHF library. It's the standard for PPO, DPO, ORPO, and other alignment training methods.

Philosophy: Research-first, alignment training native.

from trl import SFTTrainer, SFTConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset

model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")

training_args = SFTConfig(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=2,
    gradient_accumulation_steps=4,
    learning_rate=2e-4,
    logging_steps=10,
)

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    args=training_args,
    train_dataset=load_dataset("tatsu-lab/alpaca", split="train"),
)

trainer.train()

---

Speed Benchmarks

Training Speed Comparison (tokens/second)

Test setup: LLaMA 3.1 8B, LoRA r=16, 4-bit quantization, batch size 4, A100 80GB

Tool	Tokens/sec	vs Baseline	Memory (VRAM)
Unsloth (4-bit)	~4,200	2.8×	~8GB
Axolotl (QLoRA)	~1,500	1.0×	~16GB
LLaMA-Factory (QLoRA)	~1,480	~1.0×	~16GB
TRL (QLoRA)	~1,450	~0.97×	~18GB
Unsloth (full 16-bit)	~2,800	1.9×	~22GB

Unsloth advantage is real: 2-5× speed comes from custom Triton kernels for attention, cross-entropy, RoPE, and LoRA. Not just marketing.

VRAM Usage Comparison

Training LLaMA 3.1 8B, sequence length 2048:

Method	Unsloth	Axolotl	LLaMA-Factory	TRL
Full fine-tune (bf16)	60GB	70GB	72GB	74GB
LoRA (bf16)	18GB	24GB	25GB	26GB
QLoRA (4-bit)	8GB	16GB	16GB	18GB
QLoRA (4-bit, long ctx)	12GB	24GB	24GB	26GB

Minimum GPU for 8B model:

• Unsloth: RTX 3080 (10GB) ✅

• Others: RTX 3090 (24GB) required

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Supported Models

Model Support Matrix

Model Family	Unsloth	Axolotl	LLaMA-Factory	TRL
LLaMA 3.x	✅	✅	✅	✅
LLaMA 2	✅	✅	✅	✅
Mistral	✅	✅	✅	✅
Mixtral MoE	✅	✅	✅	✅
Gemma 2	✅	✅	✅	✅
Phi-3/3.5	✅	✅	✅	✅
Qwen 2.5	✅	✅	✅	✅
DeepSeek	✅	✅	✅	✅
Falcon	✅	✅	✅	✅
GPT-NeoX	Partial	✅	✅	✅
T5/FLAN	❌	✅	✅	✅
BERT/RoBERTa	❌	✅	✅	✅
Vision LLMs	Partial	Partial	✅	✅

Training Method Support

Method	Unsloth	Axolotl	LLaMA-Factory	TRL
Full fine-tune	✅	✅	✅	✅
LoRA	✅	✅	✅	✅
QLoRA	✅	✅	✅	✅
DoRA	✅	✅	✅	❌
PEFT	✅	✅	✅	✅
SFT	✅	✅	✅	✅ (native)
DPO	✅	✅	✅	✅ (native)
PPO	❌	✅	✅	✅ (native)
ORPO	✅	✅	✅	✅
KTO	❌	✅	✅	✅ (native)
GRPO	✅	❌	✅	✅
CPT (continued pretraining)	✅	✅	✅	✅

---

Unsloth: Deep Dive

What Makes It Fast

1. Triton kernels: Rewrites Flash Attention, cross-entropy loss, and LoRA in Triton

2. Fused operations: Combines multiple CUDA ops into one kernel

3. Smart gradient checkpointing: "unsloth" mode saves ~30% more memory

4. Efficient backprop: Avoids materializing large intermediate tensors

Installation on Clore.ai

# CUDA 12.8
pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps trl peft accelerate bitsandbytes

# Or with conda
conda create --name unsloth_env python=3.11
conda activate unsloth_env
conda install pytorch-cuda=12.1 pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers -y
pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps trl peft accelerate bitsandbytes

Complete Training Script

from unsloth import FastLanguageModel
from trl import SFTTrainer
from transformers import TrainingArguments
from datasets import load_dataset
import torch

# 1. Load model with Unsloth optimization
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Meta-Llama-3.1-8B-Instruct",
    max_seq_length=2048,
    dtype=None,        # Auto-detect
    load_in_4bit=True,
)

# 2. Add LoRA adapter
model = FastLanguageModel.get_peft_model(
    model,
    r=16,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
                    "gate_proj", "up_proj", "down_proj"],
    lora_alpha=16,
    lora_dropout=0,
    bias="none",
    use_gradient_checkpointing="unsloth",
    random_state=3407,
)

# 3. Load and format dataset
dataset = load_dataset("tatsu-lab/alpaca", split="train")

def format_prompt(example):
    return {"text": f"### Instruction:\n{example['instruction']}\n\n### Response:\n{example['output']}"}

dataset = dataset.map(format_prompt)

# 4. Train
trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset,
    dataset_text_field="text",
    max_seq_length=2048,
    args=TrainingArguments(
        per_device_train_batch_size=2,
        gradient_accumulation_steps=4,
        warmup_steps=5,
        num_train_epochs=1,
        learning_rate=2e-4,
        fp16=not torch.cuda.is_bf16_supported(),
        bf16=torch.cuda.is_bf16_supported(),
        logging_steps=1,
        optim="adamw_8bit",
        weight_decay=0.01,
        lr_scheduler_type="linear",
        seed=3407,
        output_dir="outputs",
    ),
)
trainer.train()

# 5. Save
model.save_pretrained("lora_model")
model.save_pretrained_gguf("model_gguf", tokenizer, quantization_method="q4_k_m")

Weaknesses: No PPO, limited to supported model list, LGPL license (check for commercial use)

---

Axolotl: Deep Dive

Configuration-First Approach

Axolotl shines when you want reproducible, version-controlled training configurations:

# axolotl_config.yml — full example
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

# Data
datasets:
  - path: tatsu-lab/alpaca
    type: alpaca
  - path: ./my_custom_data.jsonl
    type: sharegpt
dataset_prepared_path: ./prepared_data
val_set_size: 0.01

# Quantization
load_in_4bit: true
adapter: qlora
bf16: true
tf32: true

# LoRA
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj
  - gate_proj
  - up_proj
  - down_proj

# Training
sequence_len: 4096
sample_packing: true  # Packs short sequences for efficiency
pad_to_sequence_len: true
micro_batch_size: 2
gradient_accumulation_steps: 4
num_epochs: 3
learning_rate: 0.0002
optimizer: adamw_bnb_8bit
lr_scheduler: cosine

# Logging
logging_steps: 10
eval_steps: 100
save_steps: 100
output_dir: ./outputs/my-model

# wandb
wandb_project: my-fine-tune
wandb_run_id: run-001

# Install and run
pip install axolotl[flash-attn,deepspeed]
axolotl train axolotl_config.yml

Best for: Teams that want reproducible, config-versioned training runs

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LLaMA-Factory: Deep Dive

WebUI Walkthrough

# Install
pip install llamafactory

# Launch WebUI
llamafactory-cli webui
# Open http://localhost:7860

WebUI tabs:

1. Train — configure base model, dataset, method

2. Evaluate — run MMLU, CMMLU benchmarks

3. Chat — interactive inference

4. Export — merge LoRA, quantize to GGUF

CLI Training Example

# Supervised Fine-Tuning
llamafactory-cli train \
  --stage sft \
  --model_name_or_path meta-llama/Meta-Llama-3-8B \
  --dataset alpaca_gpt4_en,glaive_toolcall_en \
  --template llama3 \
  --finetuning_type lora \
  --lora_rank 8 \
  --lora_alpha 16 \
  --lora_target all \
  --output_dir saves/llama3-lora \
  --num_train_epochs 3 \
  --per_device_train_batch_size 2 \
  --gradient_accumulation_steps 4 \
  --learning_rate 2e-4 \
  --quantization_bit 4 \
  --flash_attn fa2

# DPO Training
llamafactory-cli train \
  --stage dpo \
  --model_name_or_path meta-llama/Meta-Llama-3-8B \
  --dataset dpo_mix_en \
  --template llama3 \
  --finetuning_type lora \
  --output_dir saves/llama3-dpo

Best for: Beginners, teams wanting WebUI, DPO/RLHF without deep research knowledge

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TRL: Deep Dive

RLHF Pipeline Example

TRL is the go-to for alignment training:

from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead
from trl import DPOTrainer, DPOConfig
from datasets import load_dataset

# DPO (Direct Preference Optimization) — most common alignment method
model_name = "meta-llama/Meta-Llama-3-8B-Instruct"

dpo_config = DPOConfig(
    model_name_or_path=model_name,
    output_dir="dpo_outputs",
    num_train_epochs=1,
    per_device_train_batch_size=2,
    beta=0.1,             # KL penalty coefficient
    loss_type="sigmoid",  # or "hinge", "ipo", "kto_pair"
    learning_rate=5e-7,
)

# Load preference dataset (prompt + chosen + rejected)
dataset = load_dataset("Anthropic/hh-rlhf", split="train")

trainer = DPOTrainer(
    model=model_name,
    args=dpo_config,
    train_dataset=dataset,
)
trainer.train()

Best for: Alignment research, RLHF, DPO, PPO, ORPO implementations

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Choosing the Right Tool

Decision Flow

Need maximum speed/minimum VRAM?
  → YES → Unsloth (2-5× faster, fits on smaller GPUs)

Need alignment training (DPO/PPO/RLHF)?
  → YES → TRL or LLaMA-Factory
  → Research/custom → TRL
  → Production/easy → LLaMA-Factory

Need configuration-first reproducibility?
  → YES → Axolotl

Non-technical team or want WebUI?
  → YES → LLaMA-Factory

Just want to start quickly?
  → LLaMA-Factory or Unsloth

By Team Type

Team	Recommendation	Reason
Individual researcher	Unsloth	Speed + Jupyter notebooks
ML engineer	Axolotl	Config-driven, reproducible
Product team	LLaMA-Factory	WebUI, wide model support
Alignment team	TRL	Native RLHF primitives
Startup	Unsloth + TRL	Speed + alignment when needed

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Clore.ai GPU Recommendations

Multi-GPU 80GB-class rigs are not listed on the Clore.ai marketplace. The biggest boxes listed today are 4× RTX PRO 6000 Blackwell (96GB each, 380GB total) and 8–11× RTX 5090 (32GB each). A100 / H200 / B200 capacity is sold as bare metal on request. Check GPU Pricing & Availability before sizing a deployment.

Task	Min GPU	Recommended	Tool
7-8B LoRA (QLoRA)	RTX 3080 (10GB)	RTX 3090	Unsloth
13B LoRA	RTX 3090 (24GB)	A6000 (48GB)	Unsloth/Axolotl
70B LoRA	A100 (80GB)	2×A100	Axolotl/TRL
8B Full FT	A100 (40GB)	A100 (80GB)	Any
DPO/PPO 7B	RTX 4090 (24GB)	A6000 (48GB)	TRL

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Useful Links

• Unsloth GitHub — 23K+ stars

• Axolotl GitHub — 9K+ stars

• LLaMA-Factory GitHub — 37K+ stars

• TRL GitHub — 10K+ stars

• HuggingFace PEFT Docs

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Summary

Tool	Best for	Key advantage
Unsloth	Speed-critical training, small GPUs	2-5× faster, 70% less VRAM
Axolotl	Config-driven, reproducible runs	YAML-first, many data formats
LLaMA-Factory	100+ models, WebUI, beginners	Most model support, GUI
TRL	RLHF, DPO, alignment research	Native alignment training

For most Clore.ai use cases: start with Unsloth (speed + memory efficiency), add TRL if you need DPO or PPO alignment training.