#
gmpdtd95
Follow
Starred repositories
41
stars
written in Jupyter Notebook
Clear filter
🦜🔗 Build context-aware reasoning applications
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
🔊 Text-Prompted Generative Audio Model
10 Weeks, 20 Lessons, Data Science for All!
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable…
A guidance language for controlling large language models.
Instruct-tune LLaMA on consumer hardware
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
StableLM: Stability AI Language Models
stable diffusion webui colab
Foundational Models for State-of-the-Art Speech and Text Translation
LAVIS - A One-stop Library for Language-Vision Intelligence
QLoRA: Efficient Finetuning of Quantized LLMs
PyTorch code and models for the DINOv2 self-supervised learning method.
🤖 💬 Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
FaceChain is a deep-learning toolchain for generating your Digital-Twin.
Book_4_《矩阵力量》 | 鸢尾花书：从加减乘除到机器学习；上架！
Accepted as [NeurIPS 2024] Spotlight Presentation Paper
CoTracker is a model for tracking any point (pixel) on a video.
SoTA LLM for converting natural language questions to SQL queries
中文nlp解决方案(大模型、数据、模型、训练、推理)
Open-source and strong foundation image recognition models.
HuggingLLM, Hugging Future.
An open-source project dedicated to tracking and segmenting any objects in videos, either automatically or interactively. The primary algorithms utilized include the Segment Anything Model (SAM) fo…
A simple notebook demonstrating prompt-based music generation via Mubert API
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…
Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads
Run any Llama 2 locally with gradio UI on GPU or CPU from anywhere (Linux/Windows/Mac). Use `llama2-wrapper` as your local llama2 backend for Generative Agents/Apps.