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Canada-0-MEDITATION 公司名錄
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公司新聞:
- GitHub - huggingface peft: PEFT: State-of-the-art Parameter-Efficient . . .
Recent state-of-the-art PEFT techniques achieve performance comparable to fully fine-tuned models PEFT is integrated with Transformers for easy model training and inference, Diffusers for conveniently managing different adapters, and Accelerate for distributed training and inference for really big models
- AI-Guide-and-Demos-zh_CN Guide 14. PEFT . . . - GitHub
PEFT（Parameter-Efficient Fine-Tuning）是 Hugging Face 提供的专门用于参数高效微调的工具库。 LoRA（Low-Rank Adaptation）是 PEFT 支持的多种微调方法之一，旨在通过减少可训练参数来提高微调大模型的效率。 除此之外，PEFT 还支持其他几种常见的微调方法，包括：
- GitHub - modelscope ms-swift: Use PEFT or Full-parameter to CPT SFT DPO . . .
🍲 ms-swift is a large model and multimodal large model fine-tuning and deployment framework provided by the ModelScope community It now supports training (pre-training, fine-tuning, human alignment), inference, evaluation, quantization, and deployment for 600+ text-only large models and 400+ multimodal large models Large models include: Qwen3, Qwen3 5, InternLM3, GLM4 5, Mistral, DeepSeek
- peft src peft tuners lora config. py at main · huggingface peft
The custom LoRA module class has to be implemented by the user and follow the PEFT conventions for LoRA layers """ if self _custom_modules is None: self _custom_modules = {} self _custom_modules update (mapping) @dataclass class LoraGAConfig: """ This is the sub-configuration class to store the configuration for LoRA-GA initialization
- peft examples at main · huggingface peft · GitHub
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning - peft examples at main · huggingface peft
- GitHub - Joluck RWKV-PEFT
RWKV-PEFT [ English | 中文 ] RWKV-PEFT is the official implementation for efficient parameter fine-tuning of RWKV models, supporting various advanced fine-tuning methods across multiple hardware platforms
- GitHub - Vaibhavs10 fast-whisper-finetuning
Why Parameter Efficient Fine Tuning (PEFT)? Fine-tuning Whisper in a Google Colab Prepare Environment Load Dataset Prepare Feature Extractor, Tokenizer and Data Training and Evaluation Evaluation and Inference Fin! We present a step-by-step guide on how to fine-tune Whisper with Common Voice 13 0 dataset using 🤗 Transformers and PEFT In this Colab, we leverage PEFT and bitsandbytes to
- Awesome Adapter Resources - GitHub
This repository collects important tools and papers related to adapter methods for recent large pre-trained neural networks Adapters (aka Parameter-Efficient Transfer Learning (PETL) or Parameter-Efficient Fine-Tuning (PEFT) methods) include various parameter-efficient approaches of adapting large pre-trained models to new tasks
- GitHub - Ivan-Tang-3D Point-PEFT: (AAAI2024) Point-PEFT: Parameter . . .
Introduction We propose the Point-PEFT, a novel framework for adapting point cloud pre-trained models with minimal learnable parameters Specifically, for a pre-trained 3D model, we freeze most of its parameters, and only tune the newly added PEFT modules on downstream tasks, which consist of a Point-prior Prompt and a Geometry-aware Adapter
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