torchtune

📣 Recent updates 📣

May 2025: torchtune has added support for Qwen3 models! Check out all the configs here
April 2025: Llama4 is now available in torchtune! Try out our full and LoRA finetuning configs here
February 2025: Multi-node training is officially open for business in torchtune! Full finetune on multiple nodes to take advantage of larger batch sizes and models.
December 2024: torchtune now supports Llama 3.3 70B! Try it out by following our installation instructions here, then run any of the configs here.
November 2024: torchtune has released v0.4.0 which includes stable support for exciting features like activation offloading and multimodal QLoRA
November 2024: torchtune has added Gemma2 to its models!
October 2024: torchtune added support for Qwen2.5 models - find the configs here
September 2024: torchtune has support for Llama 3.2 11B Vision, Llama 3.2 3B, and Llama 3.2 1B models! Try them out by following our installation instructions here, then run any of the text configs here or vision configs here.

Overview 📚

torchtune is a PyTorch library for easily authoring, post-training, and experimenting with LLMs. It provides:

Hackable training recipes for SFT, knowledge distillation, DPO, PPO, GRPO, and quantization-aware training
Simple PyTorch implementations of popular LLMs like Llama, Gemma, Mistral, Phi, Qwen, and more
Best-in-class memory efficiency, performance improvements, and scaling, utilizing the latest PyTorch APIs
YAML configs for easily configuring training, evaluation, quantization or inference recipes

Post-training recipes

torchtune supports the entire post-training lifecycle. A successful post-trained model will likely utilize several of the below methods.

Supervised Finetuning (SFT)

Type of Weight Update	1 Device	>1 Device	>1 Node
Full	✅	✅	✅
LoRA/QLoRA	✅	✅	✅

Example: tune run lora_finetune_single_device --config llama3_2/3B_lora_single_device
You can also run e.g. tune ls lora_finetune_single_device for a full list of available configs.

Knowledge Distillation (KD)

Type of Weight Update	1 Device	>1 Device	>1 Node
Full	❌	❌	❌
LoRA/QLoRA	✅	✅	❌

Example: tune run knowledge_distillation_distributed --config qwen2/1.5B_to_0.5B_KD_lora_distributed
You can also run e.g. tune ls knowledge_distillation_distributed for a full list of available configs.

Reinforcement Learning / Reinforcement Learning from Human Feedback (RLHF)

Method	Type of Weight Update	1 Device	>1 Device	>1 Node
DPO	Full	❌	✅	❌
	LoRA/QLoRA	✅	✅	❌
PPO	Full	✅	❌	❌
	LoRA/QLoRA	❌	❌	❌
GRPO	Full	🚧	✅	✅
	LoRA/QLoRA	❌	❌	❌

Example: tune run lora_dpo_single_device --config llama3_1/8B_dpo_single_device
You can also run e.g. tune ls full_dpo_distributed for a full list of available configs.

Quantization-Aware Training (QAT)

Type of Weight Update	1 Device	>1 Device	>1 Node
Full	✅	✅	❌
LoRA/QLoRA	❌	✅	❌

Example: tune run qat_distributed --config llama3_1/8B_qat_lora
You can also run e.g. tune ls qat_distributed or tune ls qat_single_device for a full list of available configs.

The above configs are just examples to get you started. The full list of recipes can be found here. If you'd like to work on one of the gaps you see, please submit a PR! If there's a entirely new post-training method you'd like to see implemented in torchtune, feel free to open an Issue.

Models

For the above recipes, torchtune supports many state-of-the-art models available on the Hugging Face Hub or Kaggle Hub. Some of our supported models:

Model	Sizes
Llama4	Scout (17B x 16E) [models, configs]
Llama3.3	70B [models, configs]
Llama3.2-Vision	11B, 90B [models, configs]
Llama3.2	1B, 3B [models, configs]
Llama3.1	8B, 70B, 405B [models, configs]
Mistral	7B [models, configs]
Gemma2	2B, 9B, 27B [models, configs]
Microsoft Phi4	14B [models, configs]
Microsoft Phi3	Mini [models, configs]
Qwen3	0.6B, 1.7B, 4B, 8B, 14B, 32B [models, configs]
Qwen2.5	0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B [models, configs]
Qwen2	0.5B, 1.5B, 7B [models, configs]

We're always adding new models, but feel free to file an issue if there's a new one you would like to see in torchtune.

Memory and training speed

Below is an example of the memory requirements and training speed for different Llama 3.1 models.

Note

For ease of comparison, all the below numbers are provided for batch size 2 (without gradient accumulation), a dataset packed to sequence length 2048, and torch compile enabled.

If you are interested in running on different hardware or with different models, check out our documentation on memory optimizations here to find the right setup for you.

Model	Finetuning Method	Runnable On	Peak Memory per GPU	Tokens/sec *
Llama 3.1 8B	Full finetune	1x 4090	18.9 GiB	1650
Llama 3.1 8B	Full finetune	1x A6000	37.4 GiB	2579
Llama 3.1 8B	LoRA	1x 4090	16.2 GiB	3083
Llama 3.1 8B	LoRA	1x A6000	30.3 GiB	4699
Llama 3.1 8B	QLoRA	1x 4090	7.4 GiB	2413
Llama 3.1 70B	Full finetune	8x A100	13.9 GiB **	1568
Llama 3.1 70B	LoRA	8x A100	27.6 GiB	3497
Llama 3.1 405B	QLoRA	8x A100	44.8 GB	653

*= Measured over one full training epoch
**= Uses CPU offload with fused optimizer

Optimization flags

torchtune exposes a number of levers for memory efficiency and performance. The table below demonstrates the effects of applying some of these techniques sequentially to the Llama 3.2 3B model. Each technique is added on top of the previous one, except for LoRA and QLoRA, which do not use optimizer_in_bwd or AdamW8bit optimizer.

Baseline uses Recipe=full_finetune_single_device, Model=Llama 3.2 3B, Batch size=2, Max sequence length=4096, Precision=bf16, Hardware=A100

Technique	Peak Memory Active (GiB)	% Change Memory vs Previous	Tokens Per Second	% Change Tokens/sec vs Previous
Baseline	25.5	-	2091	-
+ Packed Dataset	60.0	+135.16%	7075	+238.40%
+ Compile	51.0	-14.93%	8998	+27.18%
+ Chunked Cross Entropy	42.9	-15.83%	9174	+1.96%
+ Activation Checkpointing	24.9	-41.93%	7210	-21.41%
+ Fuse optimizer step into backward	23.1	-7.29%	7309	+1.38%
+ Activation Offloading	21.8	-5.48%	7301	-0.11%
+ 8-bit AdamW	17.6	-19.63%	6960	-4.67%
LoRA	8.5	-51.61%	8210	+17.96%
QLoRA	4.6	-45.71%	8035	-2.13%

The final row in the table vs baseline + Packed Dataset uses 81.9% less memory with a 284.3% increase in tokens per second.

Command to reproduce final row.

tune run lora_finetune_single_device --config llama3_2/3B_qlora_single_device \
dataset.packed=True \
compile=True \
loss=torchtune.modules.loss.CEWithChunkedOutputLoss \
enable_activation_checkpointing=True \
optimizer_in_bwd=False \
enable_activation_offloading=True \
optimizer=torch.optim.AdamW \
tokenizer.max_seq_len=4096 \
gradient_accumulation_steps=1 \
epochs=1 \
batch_size=2

Installation 🛠️

torchtune is only tested with the latest stable PyTorch release (currently 2.6.0) as well as the preview nightly version, and leverages torchvision for finetuning multimodal LLMs and torchao for the latest in quantization techniques; you should install these as well.

Install stable release

# Install stable PyTorch, torchvision, torchao stable releases
pip install torch torchvision torchao
pip install torchtune

Install nightly release

# Install PyTorch, torchvision, torchao nightlies.
pip install --pre --upgrade torch torchvision torchao --index-url https://download.pytorch.org/whl/nightly/cu126 # full options are cpu/cu118/cu124/cu126/xpu/rocm6.2/rocm6.3/rocm6.4
pip install --pre --upgrade torchtune --extra-index-url https://download.pytorch.org/whl/nightly/cpu

You can also check out our install documentation for more information, including installing torchtune from source.

To confirm that the package is installed correctly, you can run the following command:

tune --help

And should see the following output:

usage: tune [-h] {ls,cp,download,run,validate} ...

Welcome to the torchtune CLI!

options:
  -h, --help            show this help message and exit

...

Get Started 🚀

To get started with torchtune, see our First Finetune Tutorial. Our End-to-End Workflow Tutorial will show you how to evaluate, quantize, and run inference with a Llama model. The rest of this section will provide a quick overview of these steps with Llama3.1.

Downloading a model

Follow the instructions on the official meta-llama repository to ensure you have access to the official Llama model weights. Once you have confirmed access, you can run the following command to download the weights to your local machine. This will also download the tokenizer model and a responsible use guide.

To download Llama3.1, you can run:

tune download meta-llama/Meta-Llama-3.1-8B-Instruct \
--output-dir /tmp/Meta-Llama-3.1-8B-Instruct \
--ignore-patterns "original/consolidated.00.pth" \
--hf-token <HF_TOKEN> \

Tip

Set your environment variable HF_TOKEN or pass in --hf-token to the command in order to validate your access. You can find your token at https://huggingface.co/settings/tokens

Running finetuning recipes

You can finetune Llama3.1 8B with LoRA on a single GPU using the following command:

tune run lora_finetune_single_device --config llama3_1/8B_lora_single_device

For distributed training, tune CLI integrates with torchrun. To run a full finetune of Llama3.1 8B on two GPUs:

tune run --nproc_per_node 2 full_finetune_distributed --config llama3_1/8B_full

Tip

Make sure to place any torchrun commands before the recipe specification. Any CLI args after this will override the config and not impact distributed training.

Modify Configs

There are two ways in which you can modify configs:

Config Overrides

You can directly overwrite config fields from the command line:

tune run lora_finetune_single_device \
--config llama2/7B_lora_single_device \
batch_size=8 \
enable_activation_checkpointing=True \
max_steps_per_epoch=128

Update a Local Copy

You can also copy the config to your local directory and modify the contents directly:

tune cp llama3_1/8B_full ./my_custom_config.yaml
Copied to ./my_custom_config.yaml

Then, you can run your custom recipe by directing the tune run command to your local files:

tune run full_finetune_distributed --config ./my_custom_config.yaml

Check out tune --help for all possible CLI commands and options. For more information on using and updating configs, take a look at our config deep-dive.

Custom Datasets

torchtune supports finetuning on a variety of different datasets, including instruct-style, chat-style, preference datasets, and more. If you want to learn more about how to apply these components to finetune on your own custom dataset, please check out the provided links along with our API docs.

Custom Devices

torchtune supports finetuning on a variety of devices, including NVIDIA GPU, Intel XPU, AMD ROCm, Apple MPS, and Ascend NPU. If you're interested in running recipes on a custom device, such as Intel XPU, follow the steps below.

Step 1: Refer to the Getting Started on Intel GPU guide to configure your environment.

Step 2: Update device information via either CLI override or config changes. You can directly overwrite config fields from the command line:

tune run lora_finetune_single_device --config llama3_1/8B_lora_single_device device=xpu

Or edit your local copy of configuration files and replace device: cuda with device: xpu

Community 🌍

torchtune focuses on integrating with popular tools and libraries from the ecosystem. These are just a few examples, with more under development:

Hugging Face Hub for accessing model weights
EleutherAI's LM Eval Harness for evaluating trained models
Hugging Face Datasets for access to training and evaluation datasets
PyTorch FSDP2 for distributed training
torchao for lower precision dtypes and post-training quantization techniques
Weights & Biases for logging metrics and checkpoints, and tracking training progress
Comet as another option for logging
ExecuTorch for on-device inference using finetuned models
bitsandbytes for low memory optimizers for our single-device recipes
PEFT for continued finetuning or inference with torchtune models in the Hugging Face ecosystem

Community Contributions

We really value our community and the contributions made by our wonderful users. We'll use this section to call out some of these contributions. If you'd like to help out as well, please see the CONTRIBUTING guide.

@SalmanMohammadi for adding a comprehensive end-to-end recipe for Reinforcement Learning from Human Feedback (RLHF) finetuning with PPO to torchtune
@fyabc for adding Qwen2 models, tokenizer, and recipe integration to torchtune
@solitude-alive for adding the Gemma 2B model to torchtune, including recipe changes, numeric validations of the models and recipe correctness
@yechenzhi for adding Direct Preference Optimization (DPO) to torchtune, including the recipe and config along with correctness checks
@Optimox for adding all the Gemma2 variants to torchtune!

Acknowledgements 🙏

The transformer code in this repository is inspired by the original Llama2 code. We also want to give a huge shout-out to EleutherAI, Hugging Face and Weights & Biases for being wonderful collaborators and for working with us on some of these integrations within torchtune. In addition, we want to acknowledge some other awesome libraries and tools from the ecosystem:

gpt-fast for performant LLM inference techniques which we've adopted out-of-the-box
llama recipes for spring-boarding the llama2 community
bitsandbytes for bringing several memory and performance based techniques to the PyTorch ecosystem
@winglian and axolotl for early feedback and brainstorming on torchtune's design and feature set.
lit-gpt for pushing the LLM finetuning community forward.
HF TRL for making reward modeling more accessible to the PyTorch community.

Citing torchtune 📝

If you find the torchtune library useful, please cite it in your work as below.

@software{torchtune,
  title = {torchtune: PyTorch's finetuning library},
  author = {torchtune maintainers and contributors},
  url = {https//github.com/pytorch/torchtune},
  license = {BSD-3-Clause},
  month = apr,
  year = {2024}
}

License

torchtune is released under the BSD 3 license. However you may have other legal obligations that govern your use of other content, such as the terms of service for third-party models.

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