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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 ❤️ 7 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 👍 7 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 🔥 23 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

liked 4 models about 1 month ago

louaaron/sedd-small

Updated Mar 7, 2024 • 10.5k • 8

onnx-community/Kokoro-82M-v1.0-ONNX

Text-to-Speech • Updated Feb 8 • 36.3k • 61

Respair/Tsukasa_Speech

Text-to-Speech • Updated 7 days ago • 46

hexgrad/Kokoro-82M

Text-to-Speech • Updated 6 days ago • 1.58M • 3.78k

liked a dataset about 1 month ago

reddgr/talking-to-chatbots-unwrapped-chats

Viewer • Updated 29 days ago • 10.8k • 284 • 1

liked a model about 1 month ago

LeonJoe13/Sonic

Updated Jan 15 • 11

liked a model about 2 months ago

mav23/gte-Qwen2-1.5B-instruct-GGUF

Sentence Similarity • Updated Oct 11, 2024 • 586 • 2

liked a dataset 2 months ago

metalure/pile-distil-vibe-labels-307k

Viewer • Updated Jan 12 • 308k • 64 • 1

liked a dataset 3 months ago

fluently-sets/ultraset

Viewer • Updated Dec 22, 2024 • 785k • 127 • 5

liked 11 models 3 months ago

opencsg/csg-wukong-1B

Text Generation • Updated Aug 3, 2024 • 43 • 13

MBZUAI/MobiLlama-1B-Chat

Text Generation • Updated Feb 28, 2024 • 119 • 25

mgat1/SmolLM2-FT-DPO

Text Generation • Updated Dec 19, 2024 • 9 • 1

Mihaiii/Bulbasaur

Sentence Similarity • Updated Apr 30, 2024 • 5.99k • 2

Infinigence/Megrez-3B-Omni

Updated Feb 14 • 50 • 130

minishlab/potion-base-4M

Updated Jan 21 • 5.53k • 7

Alibaba-NLP/gte-large-en-v1.5

Sentence Similarity • Updated Aug 9, 2024 • 3.63M • 204

pingkeest/learning2_model

Sentence Similarity • Updated Oct 25, 2024 • 6 • 2

jxm/cde-small-v1

Feature Extraction • Updated Jan 21 • 1.12k • 285

arcee-ai/raspberry-3B

Text Generation • Updated Oct 8, 2024 • 101 • 39

FrancisRing/StableAnimator

Image-to-Video • Updated 15 days ago • 62