Hugging Face
Models
Datasets
Spaces
Community
Docs
Enterprise
Pricing
Log In
Sign Up
hug
edjdhug3
Follow
0 followers
Β·
3 following
AI & ML interests
None yet
Recent Activity
reacted
to
MonsterMMORPG
's
post
with π
about 2 months ago
Qwen Image Models Training - 0 to Hero Level Tutorial - LoRA & Fine Tuning - Base & Edit Model - https://youtu.be/DPX3eBTuO_Y This is a full comprehensive step-by-step tutorial for how to train Qwen Image models. This tutorial covers how to do LoRA training and full Fine-Tuning / DreamBooth training on Qwen Image models. It covers both the Qwen Image base model and the Qwen Image Edit Plus 2509 model. This tutorial is the product of 21 days of full R&D, costing over $800 in cloud services to find the best configurations for training. Furthermore, we have developed an amazing, ultra-easy-to-use Gradio app to use the legendary Kohya Musubi Tuner trainer with ease. You will be able to train locally on your Windows computer with GPUs with as little as 6 GB of VRAM for both LoRA and Fine-Tuning. Furthermore, I have shown how to train a character (person), a product (perfume) and a style (GTA5 artworks). Tutorial Link : https://youtu.be/DPX3eBTuO_Y
reacted
to
singhsidhukuldeep
's
post
with π€―
11 months ago
Exciting breakthrough in Retrieval-Augmented Generation (RAG): Introducing MiniRAG - a revolutionary approach that makes RAG systems accessible for edge devices and resource-constrained environments. Key innovations that set MiniRAG apart: Semantic-aware Heterogeneous Graph Indexing - Combines text chunks and named entities in a unified structure - Reduces reliance on complex semantic understanding - Creates rich semantic networks for precise information retrieval Lightweight Topology-Enhanced Retrieval - Leverages graph structures for efficient knowledge discovery - Uses pattern matching and localized text processing - Implements query-guided reasoning path discovery Impressive Performance Metrics - Achieves comparable results to LLM-based methods while using Small Language Models (SLMs) - Requires only 25% of storage space compared to existing solutions - Maintains robust performance with accuracy reduction ranging from just 0.8% to 20% The researchers from Hong Kong University have also contributed a comprehensive benchmark dataset specifically designed for evaluating lightweight RAG systems under realistic on-device scenarios. This breakthrough opens new possibilities for: - Edge device AI applications - Privacy-sensitive implementations - Real-time processing systems - Resource-constrained environments The full implementation and datasets are available on GitHub: HKUDS/MiniRAG
updated
a Space
over 1 year ago
edjdhug3/talk-with-bhagavad-gita
View all activity
Organizations
None yet
spaces
11
Sort:Β Recently updated
Runtime error
Talk With Bhagavad Gita
π
Build error
Llm8 Url Extraction Info
π
Sleeping
Llm7
π’
No application file
Llm5
π
Sleeping
Llm
π
Runtime error
Chatbot 3
π
View 11 Spaces
models
2
Sort:Β Recently updated
edjdhug3/unsloth_4bit_mistral_imdb_model
Updated
Jan 21, 2024
edjdhug3/llm3
Updated
Oct 5, 2023
datasets
0
None public yet