Deep-Learning-Pytorch-Huggingface

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deep-learning-pytorch-huggingface is an open-source repository providing tutorials and examples for deep learning with PyTorch and Hugging Face. It offers instructions for fine-tuning various models like FLAN-T5 and Falcon.

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Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is deep-learning-pytorch-huggingface?

deep-learning-pytorch-huggingface is an open-source GitHub repository dedicated to providing comprehensive instructions, examples, and tutorials for individuals looking to get started with deep learning using PyTorch and Hugging Face libraries. It covers a wide range of topics, including fine-tuning large language models like FLAN-T5 and Falcon 180B with advanced techniques such as DeepSpeed ZeRO, LoRA, and Flash Attention. The repository also includes guidance on using transformers and datasets, quantizing open LLMs with optimum and GPTQ, and implementing RLHF with DPO. It's a valuable resource for learning about efficient distributed training with FSDP and Q-LoRA, as well as various inference examples for text generation and other tasks.

Best used for

Ideal for developers and researchers who need to learn and implement advanced deep learning techniques using PyTorch and Hugging Face. Especially valuable for fine-tuning large language models, optimizing distributed training, and exploring various inference strategies for AI applications.

Common actions

Learn deep learning

Fine-tune language models

Implement distributed training

Quantize LLMs

Explore inference techniques

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Capabilities

Key features

Fine-tune LLMs
Distributed training examples
Model quantization
RLHF implementation
Inference benchmarks
PyTorch integration
Hugging Face libraries

Target Audience

deep learning engineersmachine learning researchersdata scientistsai developers

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of deep learning models can I fine-tune using this repository?

This repository provides examples for fine-tuning various models, including FLAN-T5 XL/XXL, Falcon 180B, Llama 2, and Gemma. It covers techniques like DeepSpeed ZeRO, LoRA, Flash Attention, and Q-LoRA for efficient training and quantization.

Does this repository include examples for distributed training?

Yes, the repository features examples for efficiently scaling distributed training using FSDP (Fully Sharded Data Parallel) and Q-LoRA. These methods are crucial for handling large models and datasets in deep learning projects.

Are there any inference examples provided in this repository?

The repository includes several inference examples, such as Text Generation Inference, FP8 Inference Benchmarks, Idefics Inference, Llama 2 Inference, and Speculative Decoding for StarCoder GPTQ. These help users understand how to deploy and run their fine-tuned models.

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Academic Research Study Assistants Knowledge Management Scientific Computing Summarization Language Learning

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Coding & Development › Open Source & Models

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