/unsloth

Finetune Llama 3.2, Mistral, Phi & Gemma LLMs 2-5x faster with 80% less memory

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Finetune Llama 3.2, Mistral, Phi-3.5 & Gemma 2-5x faster with 80% less memory!

✨ Finetune for Free

All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, Ollama, vLLM or uploaded to Hugging Face.

Unsloth supports Free Notebooks Performance Memory use
Llama 3.2 (3B) ▶️ Start for free 2x faster 60% less
Llama 3.1 (8B) ▶️ Start for free 2x faster 60% less
Phi-3.5 (mini) ▶️ Start for free 2x faster 50% less
Gemma 2 (9B) ▶️ Start for free 2x faster 63% less
Mistral Small (22B) ▶️ Start for free 2x faster 60% less
Ollama ▶️ Start for free 1.9x faster 43% less
Mistral v0.3 (7B) ▶️ Start for free 2.2x faster 73% less
ORPO ▶️ Start for free 1.9x faster 43% less
DPO Zephyr ▶️ Start for free 1.9x faster 43% less

🦥 Unsloth.ai News

Click for more news

🔗 Links and Resources

Type Links
📚 Documentation & Wiki Read Our Docs
  Twitter (aka X) Follow us on X
💾 Installation unsloth/README.md
🥇 Benchmarking Performance Tables
🌐 Released Models Unsloth Releases
✍️ Blog Read our Blogs

⭐ Key Features

  • All kernels written in OpenAI's Triton language. Manual backprop engine.
  • 0% loss in accuracy - no approximation methods - all exact.
  • No change of hardware. Supports NVIDIA GPUs since 2018+. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc) Check your GPU! GTX 1070, 1080 works, but is slow.
  • Works on Linux and Windows via WSL.
  • Supports 4bit and 16bit QLoRA / LoRA finetuning via bitsandbytes.
  • Open source trains 5x faster - see Unsloth Pro for up to 30x faster training!
  • If you trained a model with 🦥Unsloth, you can use this cool sticker!  

🥇 Performance Benchmarking

1 A100 40GB 🤗Hugging Face Flash Attention 🦥Unsloth Open Source 🦥Unsloth Pro
Alpaca 1x 1.04x 1.98x 15.64x
LAION Chip2 1x 0.92x 1.61x 20.73x
OASST 1x 1.19x 2.17x 14.83x
Slim Orca 1x 1.18x 2.22x 14.82x
Free Colab T4 Dataset 🤗Hugging Face Pytorch 2.1.1 🦥Unsloth 🦥 VRAM reduction
Llama-2 7b OASST 1x 1.19x 1.95x -43.3%
Mistral 7b Alpaca 1x 1.07x 1.56x -13.7%
Tiny Llama 1.1b Alpaca 1x 2.06x 3.87x -73.8%
DPO with Zephyr Ultra Chat 1x 1.09x 1.55x -18.6%

💾 Installation Instructions

For stable releases, use pip install unsloth. We recommend pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" for most installations though.

Conda Installation

⚠️Only use Conda if you have it. If not, use Pip. Select either pytorch-cuda=11.8,12.1 for CUDA 11.8 or CUDA 12.1. We support python=3.10,3.11,3.12.

conda create --name unsloth_env \
    python=3.11 \
    pytorch-cuda=12.1 \
    pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers \
    -y
conda activate unsloth_env

pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps trl peft accelerate bitsandbytes
If you're looking to install Conda in a Linux environment, read here, or run the below 🔽 
mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm -rf ~/miniconda3/miniconda.sh
~/miniconda3/bin/conda init bash
~/miniconda3/bin/conda init zsh

Pip Installation

⚠️Do **NOT** use this if you have Conda. Pip is a bit more complex since there are dependency issues. The pip command is different for torch 2.2,2.3,2.4,2.5 and CUDA versions.

For other torch versions, we support torch211, torch212, torch220, torch230, torch240 and for CUDA versions, we support cu118 and cu121. For Ampere devices (A100, H100, RTX3090) and above, use cu118-ampere or cu121-ampere.

For example, if you have torch 2.4 and CUDA 12.1, use:

pip install --upgrade pip
pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git"

And other examples:

pip install "unsloth[cu121-ampere-torch240] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-ampere-torch240] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-torch240] @ git+https://github.com/unslothai/unsloth.git"

pip install "unsloth[cu121-torch230] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git"

Or, run the below in a terminal to get the optimal pip installation command:

wget -qO- https://raw.githubusercontent.com/unslothai/unsloth/main/unsloth/_auto_install.py | python -

Or, run the below manually in a Python REPL:

try: import torch
except: raise ImportError("Install torch via `pip install torch`")
from packaging.version import Version as V
v = V(torch.__version__)
cuda = str(torch.version.cuda)
is_ampere = torch.cuda.get_device_capability()[0] >= 8
if cuda != "12.1" and cuda != "11.8": raise RuntimeError(f"CUDA = {cuda} not supported!")
if   v <= V('2.1.0'): raise RuntimeError(f"Torch = {v} too old!")
elif v <= V('2.1.1'): x = 'cu{}{}-torch211'
elif v <= V('2.1.2'): x = 'cu{}{}-torch212'
elif v  < V('2.3.0'): x = 'cu{}{}-torch220'
elif v  < V('2.4.0'): x = 'cu{}{}-torch230'
elif v  < V('2.5.0'): x = 'cu{}{}-torch240'
else: raise RuntimeError(f"Torch = {v} too new!")
x = x.format(cuda.replace(".", ""), "-ampere" if is_ampere else "")
print(f'pip install --upgrade pip && pip install "unsloth[{x}] @ git+https://github.com/unslothai/unsloth.git"')

For advanced installation instructions or if you see weird errors during installations:

  1. Install torch and triton. Go to https://pytorch.org to install it. For example pip install torch torchvision torchaudio triton
  2. Confirm if CUDA is installated correctly. Try nvcc. If that fails, you need to install cudatoolkit or CUDA drivers.
  3. Install xformers manually. You can try installing vllm and seeing if vllm succeeds. Check if xformers succeeded with python -m xformers.info Go to https://github.com/facebookresearch/xformers. Another option is to install flash-attn for Ampere GPUs.
  4. Finally, install bitsandbytes and check it with python -m bitsandbytes

📜 Documentation

  • Go to our official Documentation for saving to GGUF, checkpointing, evaluation and more!
  • We support Huggingface's TRL, Trainer, Seq2SeqTrainer or even Pytorch code!
  • We're in 🤗Hugging Face's official docs! Check out the SFT docs and DPO docs!
from unsloth import FastLanguageModel 
from unsloth import is_bfloat16_supported
import torch
from trl import SFTTrainer
from transformers import TrainingArguments
from datasets import load_dataset
max_seq_length = 2048 # Supports RoPE Scaling interally, so choose any!
# Get LAION dataset
url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl"
dataset = load_dataset("json", data_files = {"train" : url}, split = "train")

# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
fourbit_models = [
    "unsloth/mistral-7b-v0.3-bnb-4bit",      # New Mistral v3 2x faster!
    "unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
    "unsloth/llama-3-8b-bnb-4bit",           # Llama-3 15 trillion tokens model 2x faster!
    "unsloth/llama-3-8b-Instruct-bnb-4bit",
    "unsloth/llama-3-70b-bnb-4bit",
    "unsloth/Phi-3-mini-4k-instruct",        # Phi-3 2x faster!
    "unsloth/Phi-3-medium-4k-instruct",
    "unsloth/mistral-7b-bnb-4bit",
    "unsloth/gemma-7b-bnb-4bit",             # Gemma 2.2x faster!
] # More models at https://huggingface.co/unsloth

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/llama-3-8b-bnb-4bit",
    max_seq_length = max_seq_length,
    dtype = None,
    load_in_4bit = True,
)

# Do model patching and add fast LoRA weights
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, # Supports any, but = 0 is optimized
    bias = "none",    # Supports any, but = "none" is optimized
    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
    random_state = 3407,
    max_seq_length = max_seq_length,
    use_rslora = False,  # We support rank stabilized LoRA
    loftq_config = None, # And LoftQ
)

trainer = SFTTrainer(
    model = model,
    train_dataset = dataset,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    tokenizer = tokenizer,
    args = TrainingArguments(
        per_device_train_batch_size = 2,
        gradient_accumulation_steps = 4,
        warmup_steps = 10,
        max_steps = 60,
        fp16 = not is_bfloat16_supported(),
        bf16 = is_bfloat16_supported(),
        logging_steps = 1,
        output_dir = "outputs",
        optim = "adamw_8bit",
        seed = 3407,
    ),
)
trainer.train()

# Go to https://github.com/unslothai/unsloth/wiki for advanced tips like
# (1) Saving to GGUF / merging to 16bit for vLLM
# (2) Continued training from a saved LoRA adapter
# (3) Adding an evaluation loop / OOMs
# (4) Customized chat templates

DPO Support

DPO (Direct Preference Optimization), PPO, Reward Modelling all seem to work as per 3rd party independent testing from Llama-Factory. We have a preliminary Google Colab notebook for reproducing Zephyr on Tesla T4 here: notebook.

We're in 🤗Hugging Face's official docs! We're on the SFT docs and the DPO docs!

from unsloth import FastLanguageModel, PatchDPOTrainer
from unsloth import is_bfloat16_supported
PatchDPOTrainer()
import torch
from transformers import TrainingArguments
from trl import DPOTrainer

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/zephyr-sft-bnb-4bit",
    max_seq_length = max_seq_length,
    dtype = None,
    load_in_4bit = True,
)

# Do model patching and add fast LoRA weights
model = FastLanguageModel.get_peft_model(
    model,
    r = 64,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 64,
    lora_dropout = 0, # Supports any, but = 0 is optimized
    bias = "none",    # Supports any, but = "none" is optimized
    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
    random_state = 3407,
    max_seq_length = max_seq_length,
)

dpo_trainer = DPOTrainer(
    model = model,
    ref_model = None,
    args = TrainingArguments(
        per_device_train_batch_size = 4,
        gradient_accumulation_steps = 8,
        warmup_ratio = 0.1,
        num_train_epochs = 3,
        fp16 = not is_bfloat16_supported(),
        bf16 = is_bfloat16_supported(),
        logging_steps = 1,
        optim = "adamw_8bit",
        seed = 42,
        output_dir = "outputs",
    ),
    beta = 0.1,
    train_dataset = YOUR_DATASET_HERE,
    # eval_dataset = YOUR_DATASET_HERE,
    tokenizer = tokenizer,
    max_length = 1024,
    max_prompt_length = 512,
)
dpo_trainer.train()

🥇 Detailed Benchmarking Tables

  • Click "Code" for fully reproducible examples
  • "Unsloth Equal" is a preview of our PRO version, with code stripped out. All settings and the loss curve remains identical.
  • For the full list of benchmarking tables, go to our website
1 A100 40GB 🤗Hugging Face Flash Attention 2 🦥Unsloth Open Unsloth Equal Unsloth Pro Unsloth Max
Alpaca 1x 1.04x 1.98x 2.48x 5.32x 15.64x
code Code Code Code Code
seconds 1040 1001 525 419 196 67
memory MB 18235 15365 9631 8525
% saved 15.74 47.18 53.25

Llama-Factory 3rd party benchmarking

  • Link to performance table. TGS: tokens per GPU per second. Model: LLaMA2-7B. GPU: NVIDIA A100 * 1. Batch size: 4. Gradient accumulation: 2. LoRA rank: 8. Max length: 1024.
Method Bits TGS GRAM Speed
HF 16 2392 18GB 100%
HF+FA2 16 2954 17GB 123%
Unsloth+FA2 16 4007 16GB 168%
HF 4 2415 9GB 101%
Unsloth+FA2 4 3726 7GB 160%

Performance comparisons between popular models

Click for specific model benchmarking tables (Mistral 7b, CodeLlama 34b etc.)

Mistral 7b

1 A100 40GB Hugging Face Flash Attention 2 Unsloth Open Unsloth Equal Unsloth Pro Unsloth Max
Mistral 7B Slim Orca 1x 1.15x 2.15x 2.53x 4.61x 13.69x
code Code Code Code Code
seconds 1813 1571 842 718 393 132
memory MB 32853 19385 12465 10271
% saved 40.99 62.06 68.74

CodeLlama 34b

1 A100 40GB Hugging Face Flash Attention 2 Unsloth Open Unsloth Equal Unsloth Pro Unsloth Max
Code Llama 34B OOM ❌ 0.99x 1.87x 2.61x 4.27x 12.82x
code ▶️ Code Code Code Code
seconds 1953 1982 1043 748 458 152
memory MB 40000 33217 27413 22161
% saved 16.96 31.47 44.60

1 Tesla T4

1 T4 16GB Hugging Face Flash Attention Unsloth Open Unsloth Pro Equal Unsloth Pro Unsloth Max
Alpaca 1x 1.09x 1.69x 1.79x 2.93x 8.3x
code ▶️ Code Code Code Code
seconds 1599 1468 942 894 545 193
memory MB 7199 7059 6459 5443
% saved 1.94 10.28 24.39

2 Tesla T4s via DDP

2 T4 DDP Hugging Face Flash Attention Unsloth Open Unsloth Equal Unsloth Pro Unsloth Max
Alpaca 1x 0.99x 4.95x 4.44x 7.28x 20.61x
code ▶️ Code Code Code
seconds 9882 9946 1996 2227 1357 480
memory MB 9176 9128 6904 6782
% saved 0.52 24.76 26.09

Performance comparisons on 1 Tesla T4 GPU:

Click for Time taken for 1 epoch

One Tesla T4 on Google Colab bsz = 2, ga = 4, max_grad_norm = 0.3, num_train_epochs = 1, seed = 3047, lr = 2e-4, wd = 0.01, optim = "adamw_8bit", schedule = "linear", schedule_steps = 10

System GPU Alpaca (52K) LAION OIG (210K) Open Assistant (10K) SlimOrca (518K)
Huggingface 1 T4 23h 15m 56h 28m 8h 38m 391h 41m
Unsloth Open 1 T4 13h 7m (1.8x) 31h 47m (1.8x) 4h 27m (1.9x) 240h 4m (1.6x)
Unsloth Pro 1 T4 3h 6m (7.5x) 5h 17m (10.7x) 1h 7m (7.7x) 59h 53m (6.5x)
Unsloth Max 1 T4 2h 39m (8.8x) 4h 31m (12.5x) 0h 58m (8.9x) 51h 30m (7.6x)

Peak Memory Usage

System GPU Alpaca (52K) LAION OIG (210K) Open Assistant (10K) SlimOrca (518K)
Huggingface 1 T4 7.3GB 5.9GB 14.0GB 13.3GB
Unsloth Open 1 T4 6.8GB 5.7GB 7.8GB 7.7GB
Unsloth Pro 1 T4 6.4GB 6.4GB 6.4GB 6.4GB
Unsloth Max 1 T4 11.4GB 12.4GB 11.9GB 14.4GB
Click for Performance Comparisons on 2 Tesla T4 GPUs via DDP: **Time taken for 1 epoch**

Two Tesla T4s on Kaggle bsz = 2, ga = 4, max_grad_norm = 0.3, num_train_epochs = 1, seed = 3047, lr = 2e-4, wd = 0.01, optim = "adamw_8bit", schedule = "linear", schedule_steps = 10

System GPU Alpaca (52K) LAION OIG (210K) Open Assistant (10K) SlimOrca (518K) *
Huggingface 2 T4 84h 47m 163h 48m 30h 51m 1301h 24m *
Unsloth Pro 2 T4 3h 20m (25.4x) 5h 43m (28.7x) 1h 12m (25.7x) 71h 40m (18.1x) *
Unsloth Max 2 T4 3h 4m (27.6x) 5h 14m (31.3x) 1h 6m (28.1x) 54h 20m (23.9x) *

Peak Memory Usage on a Multi GPU System (2 GPUs)

System GPU Alpaca (52K) LAION OIG (210K) Open Assistant (10K) SlimOrca (518K) *
Huggingface 2 T4 8.4GB | 6GB 7.2GB | 5.3GB 14.3GB | 6.6GB 10.9GB | 5.9GB *
Unsloth Pro 2 T4 7.7GB | 4.9GB 7.5GB | 4.9GB 8.5GB | 4.9GB 6.2GB | 4.7GB *
Unsloth Max 2 T4 10.5GB | 5GB 10.6GB | 5GB 10.6GB | 5GB 10.5GB | 5GB *
  • Slim Orca bsz=1 for all benchmarks since bsz=2 OOMs. We can handle bsz=2, but we benchmark it with bsz=1 for consistency.


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