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s3nh

s3nh

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AI & ML interests

Quantization, LLMs, Deep Learning for good. Follow me if you like my work. Patreon.com/s3nh

Recent Activity

reacted to as-cle-bert's post with ❤️ 8 days ago

I just released a fully automated evaluation framework for your RAG applications!📈 GitHub 👉 https://github.com/AstraBert/diRAGnosis PyPi 👉 https://pypi.org/project/diragnosis/ It's called 𝐝𝐢𝐑𝐀𝐆𝐧𝐨𝐬𝐢𝐬 and is a lightweight framework that helps you 𝗱𝗶𝗮𝗴𝗻𝗼𝘀𝗲 𝘁𝗵𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝗳 𝗟𝗟𝗠𝘀 𝗮𝗻𝗱 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝗥𝗔𝗚 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀. You can launch it as an application locally (it's Docker-ready!🐋) or, if you want more flexibility, you can integrate it in your code as a python package📦 The workflow is simple: 🧠 You choose your favorite LLM provider and model (supported, for now, are Mistral AI, Groq, Anthropic, OpenAI and Cohere) 🧠 You pick the embedding models provider and the embedding model you prefer (supported, for now, are Mistral AI, Hugging Face, Cohere and OpenAI) 📄 You prepare and provide your documents ⚙️ Documents are ingested into a Qdrant vector database and transformed into a synthetic question dataset with the help of LlamaIndex 📊 The LLM is evaluated for the faithfulness and relevancy of its retrieval-augmented answer to the questions 📊 The embedding model is evaluated for hit rate and mean reciprocal ranking (MRR) of the retrieved documents And the cool thing is that all of this is 𝗶𝗻𝘁𝘂𝗶𝘁𝗶𝘃𝗲 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱: you plug it in, and it works!🔌⚡ Even cooler? This is all built on top of LlamaIndex and its integrations: no need for tons of dependencies or fancy workarounds🦙 And if you're a UI lover, Gradio and FastAPI are there to provide you a seamless backend-to-frontend experience🕶️ So now it's your turn: you can either get diRAGnosis from GitHub 👉 https://github.com/AstraBert/diRAGnosis or just run a quick and painless: ```bash uv pip install diragnosis ``` To get the package installed (lightning-fast) in your environment🏃‍♀️ Have fun and feel free to leave feedback and feature/integrations requests on GitHub issues✨

reacted to as-cle-bert's post with 👍 8 days ago

I just released a fully automated evaluation framework for your RAG applications!📈 GitHub 👉 https://github.com/AstraBert/diRAGnosis PyPi 👉 https://pypi.org/project/diragnosis/ It's called 𝐝𝐢𝐑𝐀𝐆𝐧𝐨𝐬𝐢𝐬 and is a lightweight framework that helps you 𝗱𝗶𝗮𝗴𝗻𝗼𝘀𝗲 𝘁𝗵𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝗳 𝗟𝗟𝗠𝘀 𝗮𝗻𝗱 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝗥𝗔𝗚 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀. You can launch it as an application locally (it's Docker-ready!🐋) or, if you want more flexibility, you can integrate it in your code as a python package📦 The workflow is simple: 🧠 You choose your favorite LLM provider and model (supported, for now, are Mistral AI, Groq, Anthropic, OpenAI and Cohere) 🧠 You pick the embedding models provider and the embedding model you prefer (supported, for now, are Mistral AI, Hugging Face, Cohere and OpenAI) 📄 You prepare and provide your documents ⚙️ Documents are ingested into a Qdrant vector database and transformed into a synthetic question dataset with the help of LlamaIndex 📊 The LLM is evaluated for the faithfulness and relevancy of its retrieval-augmented answer to the questions 📊 The embedding model is evaluated for hit rate and mean reciprocal ranking (MRR) of the retrieved documents And the cool thing is that all of this is 𝗶𝗻𝘁𝘂𝗶𝘁𝗶𝘃𝗲 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱: you plug it in, and it works!🔌⚡ Even cooler? This is all built on top of LlamaIndex and its integrations: no need for tons of dependencies or fancy workarounds🦙 And if you're a UI lover, Gradio and FastAPI are there to provide you a seamless backend-to-frontend experience🕶️ So now it's your turn: you can either get diRAGnosis from GitHub 👉 https://github.com/AstraBert/diRAGnosis or just run a quick and painless: ```bash uv pip install diragnosis ``` To get the package installed (lightning-fast) in your environment🏃‍♀️ Have fun and feel free to leave feedback and feature/integrations requests on GitHub issues✨

reacted to MonsterMMORPG's post with 🔥 24 days ago

Wan 2.1 Ultra Advanced Gradio APP for - Works as low as 4GB VRAM - 1-Click Installers for Windows, RunPod, Massed Compute - Batch Processing - T2V - I2V - V2V Installer and APP : https://www.patreon.com/posts/123105403 Download from here : https://www.patreon.com/posts/123105403 I have been working 14 hours today to make this APP before sleeping for you guys :) We have all the features of Wan 2.1 model Text to Video 1.3B (as low as 3.5 GB VRAM) - Really fast - 480x832px or 832x480px Video to Video 1.3B (as low as 3.5 GB VRAM) - Really fast - 480x832px or 832x480px Text to Video 14B (as low as 17 GB VRAM) - still may work at below VRAM but slower - 720x1280px or 1280x720px Image to Video 14B (as low as 17 GB VRAM) - still may work at below VRAM but slower - 720x1280px or 1280x720px When you analyze the above and below images First video is animated from the input image with following prompt A hooded wraith stands motionless in a torrential downpour, lightning cracking across the stormy sky behind it. Its face is an impenetrable void of darkness beneath the tattered hood. Rain cascades down its ragged, flowing cloak, which appears to disintegrate into wisps of shadow at the edges. The mysterious figure holds an enormous sword of pure energy, crackling with electric blue lightning that pulses and flows through the blade like liquid electricity. The weapon drags slightly on the wet ground, sending ripples of power across the puddles forming at the figure's feet. Three glowing blue gems embedded in its chest pulse in rhythm with the storm's lightning strikes, each flash illuminating the decaying, ancient fabric of its attire. The rain intensifies around the figure, droplets seemingly slowing as they near the dark entity, while forks of lightning repeatedly illuminate its imposing silhouette. The atmosphere grows heavier with each passing moment as the wraith slowly raises its crackling blade, the blue energy intensifying and casting eerie shadows

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s3nh's activity

upvoted a collection about 1 month ago

video

244 items • Updated about 22 hours ago • 6

upvoted a collection about 2 months ago

Embedding GGUFs

These are GGUF files for embedding models, which return an embedding vector of an input string instead of generating text. • 22 items • Updated Apr 26, 2024 • 8

upvoted a paper 3 months ago

A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA

Paper • 2312.03732 • Published Nov 28, 2023 • 8

upvoted a collection 5 months ago

Flux LoRA Collections

Flux THE LoRA • 130 items • Updated Jan 12 • 27

upvoted 3 papers 9 months ago

PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models

Paper • 2312.13964 • Published Dec 21, 2023 • 20

MotionBooth: Motion-Aware Customized Text-to-Video Generation

Paper • 2406.17758 • Published Jun 25, 2024 • 19

FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Sounds

Paper • 2407.01494 • Published Jul 1, 2024 • 15

upvoted a paper 10 months ago

LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning

Paper • 2401.01325 • Published Jan 2, 2024 • 27

upvoted 2 collections about 1 year ago

Merged models

Mering experiments. • 2 items • Updated Jan 5, 2024 • 2

S3nh GGUFs collection

GGUF converted models, sorted by creation date. • 5 items • Updated Jan 2, 2024 • 2

upvoted a paper over 1 year ago

To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation

Paper • 2307.15063 • Published Jul 27, 2023 • 18