Search Results for "tuning"
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Showing 10 open source projects for "tuning"
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PEFT
State-of-the-art Parameter-Efficient Fine-Tuning
Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters. Fine-tuning large-scale PLMs is often prohibitively costly. In this regard, PEFT methods only fine-tune a small number of (extra) model parameters, thereby greatly decreasing the computational and storage costs. -
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SetFit
Efficient few-shot learning with Sentence Transformers
SetFit is an efficient and prompt-free framework for few-shot fine-tuning of Sentence Transformers. It achieves high accuracy with little labeled data - for instance, with only 8 labeled examples per class on the Customer Reviews sentiment dataset, SetFit is competitive with fine-tuning RoBERTa Large on the full training set of 3k examples. -
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Llama Recipes
Scripts for fine-tuning Meta Llama3 with composable FSDP & PEFT method
The 'llama-recipes' repository is a companion to the Meta Llama models. We support the latest version, Llama 3.1, in this repository. The goal is to provide a scalable library for fine-tuning Meta Llama models, along with some example scripts and notebooks to quickly get started with using the models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama and other tools in the LLM ecosystem. The examples here showcase how to run Llama locally, in the cloud, and on-prem. -
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Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. -
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5
ModelScope
Bring the notion of Model-as-a-Service to life
...Model contributors of different areas can integrate models into the ModelScope ecosystem through the layered APIs, allowing easy and unified access to their models. Once integrated, model inference, fine-tuning, and evaluations can be done with only a few lines of code. -
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Adapters
A Unified Library for Parameter-Efficient Learning
Adapters is an add-on library to HuggingFace's Transformers, integrating 10+ adapter methods into 20+ state-of-the-art Transformer models with minimal coding overhead for training and inference. Adapters provide a unified interface for efficient fine-tuning and modular transfer learning, supporting a myriad of features like full-precision or quantized training (e.g. Q-LoRA, Q-Bottleneck Adapters, or Q-PrefixTuning), adapter merging via task arithmetics or the composition of multiple adapters via composition blocks, allowing advanced research in parameter-efficient transfer learning for NLP tasks. -
7
SAHI
A lightweight vision library for performing large object detection
...Such objects are represented by small number of pixels in the image and lack sufficient details, making them difficult to detect using conventional detectors. In this work, an open-source framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides a generic slicing aided inference and fine-tuning pipeline for small object detection. -
8
LLaVA
Visual Instruction Tuning: Large Language-and-Vision Assistant
Visual instruction tuning towards large language and vision models with GPT-4 level capabilities. -
9
Petals
Run 100B+ language models at home, BitTorrent-style
...Petals runs large language models like BLOOM-176B collaboratively — you load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning. -
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Lightning Bolts
Toolbox of models, callbacks, and datasets for AI/ML researchers
Bolts package provides a variety of components to extend PyTorch Lightning, such as callbacks & datasets, for applied research and production. Torch ORT converts your model into an optimized ONNX graph, speeding up training & inference when using NVIDIA or AMD GPUs. We can introduce sparsity during fine-tuning with SparseML, which ultimately allows us to leverage the DeepSparse engine to see performance improvements at inference time.
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