Search: “models”

We gave two small LLMs a real customer-service task. One got 100/100. The other stopped with the job half-done. I wanted to test something more meaningful than benchmark scores: can a small model actu

Saw the new Introducing System One Models & Jev - TypeSafe AI Blog Jev model which just outputs probabilities given choices and I thought it sounded a something the Qwen reranker models could do? Then

I am planning to buy hardware to run local models. I'm not sure what kind of hardware is best and what will I be able to run. I'm torn between getting a used M1 Max 64gb Macbook for €1500, M3 Max 128g

Hey r/LocalLLaMA, We’ve released our full ShapeLearn GGUFs for Qwen 3.8 27B. Blog / Download models TL;DR 3.84 bpw (GPU-5) reaches 99.63% of BF16’s aggregate score of 8 benchmarks, being the most accu

EDIT: Sorry for the unclear title. This model is UkisAI's Swift-Qwen3.8-27B, not a new version of BottleCap AI's 3.6-ThinkingCap. All credit goes to UkisAI for making great fine-tune, and I made this

Two months ago I announced I had found a new dynamic quant method called Voodoo Quant which was SOTA for the most aggressive quant levels on some smaller Qwen3.5 GGUF models. I kept the methodology pr

Just stumbled upon this. Seems to be a new 3B TTS model mainly focused on Indian languages (22 Indic languages + English). Apparently supports code-switching, romanized text, emotion/delivery control

After noticing that it is ranked among MUCH larger frontier models in the EQ-Bench Creative Writing benchmark and the Hemingway-bench, I decided to give it a try and was very impressed. I didn't do ve

I was reading up on the recent controversy around Tristan Buckmaster, Levent Alpöge, OpenAI, and the Navier–Stokes result, and it got me thinking about something broader than this particular dispute.

I think that mall models between 10B and 40B are the real gamechanger. These models will be the ones that will make the AI buble pop and big companies like Anthropic and OpenAi go to hell. We are curr

Liquid AI currently possesses among the fastest LLM architectures around, and some of the best SLMs (in terms of utility IMO) around, so I'm very excited to see what a potential 100B LFM (3?) model wo

AirLLM dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card — without quantization, distillation, or pruning. You can even run 405B Llama 3.1 on

A majority of users in this sub use LLMs for coding/agentic tasks and I see why a lot of value is put into them but many try to say 'Well coding has improved therefore it can just use tool calling and

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

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

https://preview.redd.it/xxjh11f38jih1.png?width=1852&format=png&auto=webp&s=76850ed51e29a8bc86c2ca718d4320075eed4363 Just wanted to share a user report that I found to be very interesting. Some person

Hey. Just tried it on my old ass gpus 😄 Surprisingly Tensor Split is working on 2 gpus almost doubling PP (wonder how it will work with 4 gpus) Q6 — 1 GPU llama-server --model <MODEL_DIR>/Muse-Glimmer

I serve a free local model on my Mac Mini and route real agent work to it. To check I was not fooling myself, I set up a blind grader that replays frontier tasks locally and scores both. https://previ

I know qwen 3.5 4b is great but a bit too large and miniPCM5 1b is great for agentic use but not so great for multilingual natural language understanding. Google eXb variants are just too big in total

I don't know much about llms aside from downloading them through a frontend and running them on my laptop or potato phone. Google released gemma 4, but unlike gemma 3, there isn't a 1b model this time

Hello, I have a friend who's using various local LLM's like qwen3.6 27B, 35b-a3b, North Mini Code, and qwen2.5-vl-7b (just for vision). They have a use case where they're trying to have an LLM drive a

The Test Bench Setup I tested running a massive 1.56TB Mixture-of-Experts (MoE) checkpoint (96 shards, 93 layers, 896 experts/layer, ~4.46 bits/param MXFP4) on a budget gaming laptop. Laptop: HP Victu

I've spent the last year or so playing around with open source MLX and GGUF models on iPhone hardware. Given the limitations in memory, GPU/CPU/ANE, and in turn the context window I've been trying to

https://reddit.com/link/1v5rvuq/video/bgmwc754i9fh1/player My goal was to create a benchmark to measure the spatial awareness and memory of models. Eventually, I came up with the simple idea of a maze

I just had a thought while scrolling. No idea if this already exists. What if users trained an AI model together—a bit like the Tor network or Bitcoin mining back in the day? - Participants download a

MODEL + GGUF : https://huggingface.co/InternScience/models?search=a1-4b Technical Report Benchmark Qwen3.5-4B Agents-A1-4B Qwen3.5 Qwen3.6 Nex-N2-mini Agents-A1 🧠 Dense Models (~4B) 🔀 MoE Models (35B-

A developer created a local Model Context Protocol (MCP) memory server that integrates Ollama to provide AI coding assistants with persistent memory capabilities while maintaining complete privacy and

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

This discussion thread examines which image generation models provide the best native seed variation capabilities—the ability to generate diverse images from the same prompt by varying the seed parame

Parllama is a TUI (Text UI) application designed for easy management and use of Ollama-based LLMs that also works with major cloud-provided LLMs. It provides core model management features including f

LLaDA2.0-Uni is a unified diffusion large language model (dLLM) based on Mixture-of-Experts architecture that seamlessly integrates multimodal understanding and generation. The model supports text-to-

This discussion likely covers model selection guidance for users creating storytelling visuals with Stable Diffusion, addressing factors like stylistic output and consistency . The r/StableDiffusion c

This r/StableDiffusion thread discusses community comparisons of video generation models (such as those built on Stable Diffusion or FLUX architectures) evaluated specifically for how well they preser

This Reddit thread from r/StableDiffusion discusses the AI tools behind the viral Lego-style propaganda videos produced by Explosive Media, known in Persian as Akhbar Enfejari — a group whose AI-gener

Waypoint-1.5 New open source world model trained on FPS games to run on local consumer GPUs at 60fps

This Reddit thread likely discusses community questions around OpenAI's new 100/month ChatGPT Pro tier and its implications for access to GPT-5.4 Pro. OpenAI introduced a 100/month Pro tier positioned

This Reddit post from r/MachineLearning announces a build-from-scratch Python book focused on the engineering infrastructure surrounding AI systems — the components beyond the model call itself, such

This r/ollama thread discusses users experiencing erratic and broken behavior when running Google's Gemma 4 models locally via Ollama, including issues such as strange and unrelated responses, as well

This Reddit thread from r/StableDiffusion addresses a common point of confusion among users of Stable Diffusion UIs (such as Stable Diffusion WebUI Forge) regarding whether selecting a 'UI Preset' is

Uncompressed image: https://postimg.cc/gallery/6WBdT03 (To view the uncompressed image, click on it a second time after opening it, or simply download the entire image album) -------------------------

came across a pretty interesting technical breakdown of chatgpt's newly launched 'intelligent ui' feature, and thought this subreddit might find it interesting. for anyone unfamiliar with the concept,

tldr: Open sourced my prompt to game suite: https://github.com/ndamiano/ai-agent-test, it's MIT licensed, and this runs well on my 5090 with 64gb ram, but any model that can handle tool calls can mana

A small thing I noticed: full-size screenshots use far more AI tokens than the model needs, so usage limits run out sooner. I built a free Firefox add-on, TokenSaver, that shrinks images before they a

guys do you still remember K2 Horizon? https://huggingface.co/IFM/K2-Horizon-MoVA-36B-A4B-GGUF https://huggingface.co/IFM/K2-Horizon-32B-GGUF https://huggingface.co/IFM/K2-Horizon-3.7B-GGUF https://hu

​ From the first LLaMA 33B I knew I wanted that magic-like intelligence locally, mine, so nobody could take it away when I needed it. I bought a 3090 for my home PC. Then LLaMA 65B appeared and I was

A while ago I asked here how to turn ~5 million court decisions into structured graphs without running an expensive LLM on every document thanks for the advice . I went with the 'small extractor + cla