Dawn333 (Dawn-333)

Dawn-333

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PaddleNLP

👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.

Language:PythonLicense:Apache-2.0Stargazers:11803Issues:0Issues:0

haodf

好大夫在线问答爬取

Language:PythonLicense:MITStargazers:23Issues:0Issues:0

LocalAI

:robot: The free, Open Source alternative to OpenAI, Claude and others. Self-hosted and local-first. Drop-in replacement for OpenAI, running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. Features: Generate Text, Audio, Video, Images, Voice Cloning, Distributed inference

Language:C++License:MITStargazers:22234Issues:0Issues:0

DeepMatch

A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.

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blueprints-text

Jupyter notebooks for our O'Reilly book "Blueprints for Text Analysis Using Python"

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EMO

Emote Portrait Alive: Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions

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bert

TensorFlow code and pre-trained models for BERT

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MUSAE

The reference implementation of "Multi-scale Attributed Node Embedding". (Journal of Complex Networks 2021)

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Alpaca-CoT

We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. We welcome open-source enthusiasts to initiate any meaningful PR on this repo and integrate as many LLM related technologies as possible. 我们打造了方便研究人员上手和使用大模型等微调平台,我们欢迎开源爱好者发起任何有意义的pr!

Language:Jupyter NotebookLicense:Apache-2.0Stargazers:2535Issues:0Issues:0

LLM-Adapters

Code for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models"

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LLaMA-Adapter

[ICLR 2024] Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters

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