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v0.5.0

pytorch/torchtune

版本发布时间: 2024-12-21 04:50:31

pytorch/torchtune最新发布版本:v0.5.0(2024-12-21 04:50:31)

Highlights

We are releasing torchtune v0.5.0 with lots of exciting new features! This includes Kaggle integration, a QAT + LoRA training recipe, improved integrations with Hugging Face and vLLM, Gemma2 models, a recipe enabling finetuning for LayerSkip via early exit, and support for NPU devices.

Kaggle integration (#2002)

torchtune is proud to announce our integration with Kaggle! You can now finetune all your favorite models using torchtune in Kaggle notebooks with Kaggle model hub integration. Download a model from the Kaggle Hub, finetune on your dataset with any torchtune recipe, then pick your best model and upload your best checkpoint to the Kaggle Hub to share with the community. Check out our example Kaggle notebook here to get started!

QAT + LoRA training recipe (#1931)

If you've seen the Llama 3.2 quantized models, you may know that they were trained using quantization-aware training with LoRA adapters. This is an effective way to maintain good model performance when you need to quantize for on-device inference. Now you can train your own quant-friendly LoRA models in torchtune with our QAT + LoRA recipe!

To finetune Llama 3.2 3B with QAT + LoRA, you can run:

# Download Llama 3.2 3B
tune download meta-llama/Llama-3.2-3B-Instruct --ignore-patterns "original/consolidated.00.pth"

# Finetune on two devices
tune run --nproc_per_node 2 qat_lora_finetune_distributed --config llama3_2/3B_qat_lora

Improved Hugging Face and vLLM integration (#2074)

We heard your feedback, and we're happy to say that it's now easier than ever to load your torchtune models into Hugging Face or vLLM! It's as simple as:

from transformers import AutoModelForCausalLM

trained_model_path = "/path/to/my/torchtune/checkpoint"

model = AutoModelForCausalLM.from_pretrained(
    pretrained_model_name_or_path=trained_model_path,
)

See the full examples in our docs! Hugging Face, vLLM

Gemma 2 models (#1835)

We now support models from the Gemma 2 family! This includes the 2B, 9B, and 27B sizes, with recipes for full, LoRA, and QLoRA finetuning on one or more devices. For example, you can finetune Gemma 2 27B with QLoRA by running:

# Download Gemma 2 27B
tune download google/gemma-2-27b --ignore-patterns "gemma-2-27b.gguf"

# Finetune on a single GPU
tune run lora_finetune_single_device --config gemma2/27B_qlora_single_device

A huge thanks to @Optimox for landing these models!

Early exit training recipe (#1076)

LayerSkip is an end-to-end solution to speed up LLM inference. By combining layer dropout with an appropriate dropout schedule and using an early exit loss during training, you can increase the accuracy of early exit at inference time. You can use our early exit config to reproduce experiments from LayerSkip, LayerDrop, and other papers.

You can try torchtune's early exit recipe by running the following:

# Download Llama2 7B
tune download meta-llama/Llama-2-7b-hf --output-dir /tmp/Llama-2-7b-hf

# Finetune with early exit on four devices
tune run --nnodes 1 --nproc_per_node 4 dev/early_exit_finetune_distributed --config recipes/dev/7B_full_early_exit.yaml

NPU support (#1826)

We are excited to share that torchtune can now be used on Ascend NPU devices! All your favorite single-device recipes can be run as-is, with support for distributed recipes coming later. A huge thanks to @noemotiovon for their work to enable this!

What's Changed

New Contributors

Full Changelog: https://github.com/pytorch/torchtune/compare/v0.4.0...v0.5.0

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