Knowledge catalogue
Retrieval benchmarks sometimes feel benchmaxxed by models, so we wanted to find a way to tie it as close as possible to my objective: finding the article that answers a product question right. We work
https://preview.redd.it/3p6234jzk2oh1.png?width=900&format=png&auto=webp&s=b9eab3351d6c6dbd4d5b3fd677a6c40b57f18167 https://huggingface.co/tencent/EVIE-8B 🌟 Highlights SOTA Retrieval Performance: 66.7
Moss is a YC-backed high-performance runtime for real-time semantic search that delivers sub-10ms lookups, instant index updates, and zero infrastructure overhead, running where the agent lives — clou
AetherMind is a locally-run personal memory system built by a Reddit user (r/ollama) that leverages Ollama with the `qwen2.5:7b` model and Retrieval-Augmented Generation (RAG) to allow users to query
There’s a hands-on workshop on August 29 that builds and benchmarks this properly, end to end, using entirely open models, no API calls involved. Led by Ben Auffarth, AI Consultant and Founder of Chel
Overview EVIE-Preview-4.5B is a state-of-the-art multilingual Visual Document Retrieval (VDR) model built upon Qwen3.5-4B. It employs ColBERT-style late interaction with native 128-dimensional multi-v
Original Article - https://x.com/p_nawrot/status/2089315591010079034 I've spent the last few years working on efficient attention and KV Cache Compression. I've read many papers, dug deep into referen
EmbeddingGemma 2 came out this week. It maps images and text into one 768-dim space, so you can search photos by describing them. I ported its text and vision towers to ruNNtime, a WebGPU inference li
I’ve been building this for a long fucking time, and tonight I finally released Memoria 1.0.0. I built it because I actually wanted to use it. I wanted a real memory layer for local LLM applications t
Most agent memory setups run a model call on the way in. Something reads the turn, decides whether it's worth keeping, rewrites it into a 'memory', tags it with a type and an importance score. That's
Nemotron-3.5-Embed-1B-BF16 is a versatile embedding model developed by NVIDIA for multimodal and multilingual question-answering retrieval. The model can embed a document page in the form of an image,
Three weeks back , i posted SHADOW-250M here. It got 360 upvotes, 293 on r/LocalLLaMA and 94 GitHub stars. Thank you. That model was 60 MB, ran around 400 tok/s on CPU and could retrieve records from
Google Cloud published OKF (Open Knowledge Format) on June 12th — a spec for storing curated knowledge as a directory of markdown files with YAML frontmatter. One concept per file, linked to each othe
A Reddit discussion in the r/ollama community seeking recommendations for RAG (Retrieval-Augmented Generation) models, likely addressing model selection for local LLM-based document retrieval systems.
I've been running Qwen3.8-27B as a local inference server for a production content intelligence pipeline (HVAC industry stuff, lots of long-context retrieval and structured extraction). I have been wa
Quick update on the RTX 5060 Ti local LLM repo. It has changed quite a bit since my previous posts. The project started as a collection of practical notes and benchmark results. That was useful, but a
I have been running some experiments with smaller open-weight LLMs on multiple-choice questions of Swedish medical licensing exams. On a dataset called MedQA-SWE, GPT-4 scored 84% accuracy in 2024 and
Applying PCA as a rotation step before naively truncating embeddings from non-Matryoshka models like BGE-M3 can recover much of the retrieval quality that is otherwise lost when simply chopping dim...
Hybrid search, reranking, query decomposition, and query expansion are often treated as must-haves for good RAG. We wanted to see how much each actually helped, so we tested them. Same model, same emb
Hey guys, Just finished benchmarking DeepSeek V4 Flash 284B + DSpark on a single RTX PRO 6000 96GB. Short version: DSpark: ~15–17% faster generation on my coding workload On this setup, the DSpark dra
In OCP APAC 2026, Tai AMD SVP of compute and enterprise AI said agents don't cut GPU demand but they just pile on a whole extra layer of orchestration, retrieval, and tool-calling work that runs on CP
Heeeey all! I just completed some fun tests with Muse Glimmer, I thought I'd let you know. In fact, the summary below was written by Muse itself! I ran a 2× DGX Spark cluster and got Meta's day-old Mu
LFM2.5-Encoder is a family of multilingual bidirectional encoders built on the LFM2 architecture, available in two sizes: LFM2.5-Encoder-230M — a lightweight encoder for tight latency and memory budge
Retrieval-Augmented Generation (RAG) is a technique that enhances large language models (LLMs) by enabling them to access and utilize external knowledge sources during response generation. This likely
Based on the Reddit post title, this entry likely discusses common mistakes and pitfalls in setting up personal Retrieval-Augmented Generation (RAG) systems with Ollama, specifically focusing on incor
A Reddit user on r/StableDiffusion built a locally-running RAG (Retrieval-Augmented Generation) engine designed to eliminate the need for manually crafting lengthy, model-specific image generation pro
The specific Reddit post could not be retrieved directly, but based on its title 'IMAX at Home' posted in r/StableDiffusion, this is a community showcase post where a user likely used Stable Diffusion
The specific Reddit post titled 'lol' (reddit.com/r/StableDiffusion/comments/1slgcdp/) could not be retrieved directly from search results. Based on its source and the vague title, here is a likely de
This Reddit post from r/ChatGPT discusses the trade-offs between retrieving documentation via an MCP (Model Context Protocol) server versus using traditional web search within AI workflows. The core c
I was unable to retrieve the specific Reddit post at the provided URL (reddit.com/r/ChatGPT/comments/1sh9hma/lighter_joke/), as it did not appear in the search results. The search only returned gen...
The specific Reddit post (r/ChatGPT/comments/1shzwii) was not directly retrievable, but based on the closely related search results about AI pricing tiers discussed in that community, here is a fac...
I was unable to retrieve the specific Reddit thread or locate reliable sourced details about Apple's head of cloud making a statement that open source models will address 90% of use cases. The sear...
I was unable to retrieve the specific Reddit post from the URL provided, and my search did not return relevant results for that thread. Reddit content is often not indexed in real time, and I canno...
I was unable to retrieve the specific Reddit post at the provided URL through search results. The search did not surface the content of that particular thread (reddit.com/r/MachineLearning/comments...
I was unable to retrieve the specific Reddit post at the provided URL through my search. The post (reddit.com/r/StableDiffusion/comments/1shxmfk) did not surface in the search results, and I cannot...
The specific Reddit post (r/ChatGPT, post ID `1shi3fn`, titled 'Grok for you') was not directly retrievable or indexed in search results. Based on the available context from surrounding community d...
I was unable to retrieve the specific Reddit post or sufficient detail about this particular 'Guanaco' router project from search results. The Reddit URL (r/ollama/comments/1shbql8) did not surface...
I was unable to retrieve the specific Reddit post at the URL provided (reddit.com/r/ChatGPT/comments/1shtuw2), and the search results did not surface its content. The post may be removed, private, ...
I was unable to retrieve the specific Reddit post from the URL provided, and I don't have direct browsing capability to fetch URLs. The web search also did not return results for that specific post...
I was unable to retrieve the specific Reddit post from the URL provided, and my web search did not return the content of that page. Reddit posts are often not indexed in real time, and I cannot dir...
I decided to run prism-ml/Ternary-Bonsai-2-27B-PQ2_0 through my own set of UNSCIENTIFIC benchmarks. I needed something to compare it to, so I decided I would compare with another 27B model by filesize
https://github.com/paulknysh/raggy A lightweight CLI tool for Retrieval-Augmented Generation (RAG) over local documents built with Ollama. Hybrid database (vector + BM25 index) and embedding generatio
I'm building a local AI companion and I'm currently working on its cognitive layer. The goal is: User message → understand intent → decide what context is relevant → retrieve only useful memories/stat
I have been searching for suitable model to run on my 8GB RAM toy, NVIDIA Orin Nano Super 8GB. This little toy was priced at 249 earlier this year (not any more), and pulls very little power when idle
Build a 100% offline fast Retrieval Augmented Generation (RAG) system that runs without an internet connection, without cloud APIs, without OpenAI/Ollama Published a video where you can build a fully
NVIDIA Nemotron Parse 2.0 transforms document images into structured, machine-readable representations with text, layout classes, bounding boxes, and reading-order information. Given a Red, Green, Blu
I am building a domain-specific AI agent powered by Ollama (using the qwen:2.5 model). For data retrieval, the agent utilizes multiple search APIs: DuckDuckGo Search (DDGS), Tavily, Serper, and Google
This post discusses a solution for keeping multi-hop retrieval-augmented generation (RAG) systems updated when data changes, using an embed-and-append approach with the open-weights Llama-3.3-70B mode
ChatGPT's memory feature stores conversation context and user preferences within individual chats. This Reddit post discusses prompts designed to retrieve or extract the stored memory data that ChatGP
A developer documented their experience building a fully-local paper Retrieval-Augmented Generation (RAG) system using two NVIDIA GTX 1080 Ti GPUs and one RTX 3090, sharing three significant challenge
I don't have the ability to access or retrieve the content of specific Reddit posts from URLs. To write an accurate summary for your knowledge base, I would need you to either: 1. Share the text conte
Adds knowledge to Ollama models using Retrieval-Augmented Generation (RAG) , where users create a knowledge base directory with reference files like PDFs, text files, or CSVs . A custom model can be c
I don't have the ability to access Reddit posts directly to retrieve the specific content. Without knowing the actual question or topic discussed in this post, I cannot provide an accurate factual sum
This discussion explores which language models work best when combined with RAG (Retrieval-Augmented Generation) for building a locally hosted LLM focused on survival and off-grid living topics. The t
I'd need to search for this specific Reddit discussion to provide an accurate summary of what was actually discussed. Let me retrieve that information. This Reddit post discusses using AI vision model
A developer created a command-line tool that integrates Graph RAG (Retrieval-Augmented Generation) with Obsidian, using Ollama to run a local LLM for extracting entity relationships from notes. The to
I wasn't able to retrieve the specific Reddit thread from the search results. However, based on the context of the title, source ('local-ai'), and the well-known community discussion around this to...
The specific Reddit thread could not be retrieved directly, but based on its title, source, and the broader context of r/MachineLearning discussions, here is a fitting knowledge base summary: This r/M
I was unable to retrieve the specific Reddit thread content from that URL through my search. Reddit threads often require direct access to load user-generated content, and the search did not return...
The specific Reddit post could not be retrieved from the search results. However, based on the context of the URL and related results, I can provide the following best-effort summary based on what ...