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guangkechen's Projects

allosaurus icon allosaurus

Allosaurus is a pretrained universal phone recognizer for more than 2000 languages

audiolm-pytorch icon audiolm-pytorch

Implementation of AudioLM, a SOTA Language Modeling Approach to Audio Generation out of Google Research, in Pytorch

baichuan-13b icon baichuan-13b

A 13B large language model developed by Baichuan Intelligent Technology

bark icon bark

🔊 Text-Prompted Generative Audio Model

bmtools icon bmtools

Tool Learning for Big Models, Open-Source Solutions of ChatGPT-Plugins

bubogpt icon bubogpt

BuboGPT: Enabling Visual Grounding in Multi-Modal LLMs

chatlog icon chatlog

⏳ ChatLog: Recording and Analysing ChatGPT Across Time

clap icon clap

Contrastive Language-Audio Pretraining

cpad icon cpad

The official dataset of paper "Goal-Oriented Prompt Attack and Safety Evaluation for LLMs".

cross icon cross

[NeurIPS 2023] Official PyTorch implementation for the paper "CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography"

crubadan icon crubadan

Scripts and data for the Crúbadán web crawler: http://crubadan.org/

diff-pgd icon diff-pgd

[NeurIPS'2023] Official Code Repo:Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability

fast-detect-gpt icon fast-detect-gpt

Code base for "Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature".

geneface icon geneface

GeneFace: Generalized and High-Fidelity 3D Talking Face Synthesis; ICLR 2023; Official code

gptfuzz icon gptfuzz

Official repo for GPTFUZZER : Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts

grad-svc icon grad-svc

Diffusion Singing Voice Conversion based on Grad-TTS from HuaWei

groundingdino icon groundingdino

Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

imagebind icon imagebind

ImageBind One Embedding Space to Bind Them All

imagereward icon imagereward

ImageReward: Learning and Evaluating Human Preferences for Text-to-image Generation

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