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Name: Will Hsu
Type: User
Bio: Student in a university
Will Hsu's Projects
auto-gpt
An experimental open-source attempt to make GPT-4 fully autonomous.
auto-photoshop-stablediffusion-plugin
A user-friendly plug-in that makes it easy to generate stable diffusion images inside Photoshop using Automatic1111-sd-webui as a backend.
chatglm2-6b
ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型
chatmed
ChatMed: 中文医疗大模型，善于在线回答患者/用户的日常医疗相关问题！
deepfacelive
Real-time face swap for PC streaming or video calls
dify
One API for plugins and datasets, one interface for prompt engineering and visual operation, all for creating powerful AI applications.
doctorgpt
DoctorGPT is an LLM that can pass the US Medical Licensing Exam. It works offline, it's cross-platform, & your health data stays private.
firefly
Firefly(流萤): 中文对话式大语言模型(全量微调+QLoRA)，支持微调Llma2、Llama、Qwen、Baichuan、ChatGLM2、InternLM、Ziya、Bloom等大模型
flowise
Drag & drop UI to build your customized LLM flow
fooocus
Focus on prompting and generating
itlearnhall_top
Python for IOT-flask大屏展示Python项目
langchain-chinese-getting-started-guide
LangChain 的中文入门教程
llama
Inference code for LLaMA models
llama-chat
A boilerplate for creating a Llama 2 chat app
magic-edit
MagicEdit: High-Fidelity Temporally Coherent Video Editing
one-api
OpenAI 接口管理 & 分发系统，支持 Azure、Anthropic Claude、Google PaLM 2、智谱 ChatGLM、百度文心一言、讯飞星火认知、阿里通义千问以及 360 智脑，可用于二次分发管理 key，仅单可执行文件，已打包好 Docker 镜像，一键部署，开箱即用. OpenAI key management & redistribution system, using a single API for all LLMs, and features an English UI.
openai-python
The OpenAI Python library provides convenient access to the OpenAI API from applications written in the Python language.
openai-quickstart
A comprehensive guide to understanding and implementing large language models with hands-on examples using LangChain for AIGC applications.
pyecharts
🎨 Python Echarts Plotting Library
streamlit-example
Example Streamlit app that you can fork to test out share.streamlit.io
visualblocks
Visual Blocks for ML is a Google visual programming framework that lets you create ML pipelines in a no-code graph editor. You – and your users – can quickly prototype workflows by connecting drag-and-drop ML components, including models, user inputs, processors, and visualizations.
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