Search: “model-releases”

We are in a world where you can create truly unique, visually interesting and creative playable demos on demand with the current capabilities of Codex and Claude Code. We don't need to keep cloning th

We are releasing PerceptionBench, a benchmark that isolates visual perception and evaluates it as a set of atomic capabilities - discovered from how today's models fail, rather than defined in advance

Instead of 2T+ models, continuing to release highly capable small to medium size LLMs would really help to keep this community vibrant. Hardly anyone can even dream of running the recent 1.5-2T+ beast

We've open-sourced MoonEP, our high-performance communication library for distributed MoE workloads. Built to make expert-parallel communication more efficient at scale, MoonEP helps reduce communicat

arXiv:2604.00024v2 Announce Type: replace Abstract: Large language models are increasingly used for medical guidance, but women's health remains under-evaluated in benchmark design. We present the Wom

When I released Inflect v2 last week, I thought most people would ask whether a TTS model this small actually sounded decent. Instead, I kept getting two questions: “Can I train it on my own voice?” “

arXiv:2607.22014v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are emerging as core reasoning modules for embodied agents, yet it remains unclear how well general-purpose m

There is a lot of buzz around running 100B parameter models on cheap local hardware using ternary (1.58-bit) quantization like Microsoft's BitNet architecture. The theoretical hardware shortcuts are i

I am writing a fairly complex C++ windows MFC application. I have a few 3090s and can run F16 Qwen3.6-27B with 256K context and MTP. The quality of code is exceptional with this quant vs its lower qua

This is our biggest comparison yet. We've taken 23 Gemma 4 E4B models from huggingface and ran them through the abliterlitics gauntlet. We also have a new abliterlitics discord, feel free to jump on a

Last week someone here said ThinkingCap and Fable Fusion 'really do beat the OG' for agentic work, so I ran it: 6 self-grading tasks, 5 reps, 3 models, 90 isolated runs. Tooling, since that's half the

Announcing the Claude Code-compatible interface for our new Fugu-Ultra v1.1! 🐡 Put a dynamically coordinated team of frontier models to work inside the coding workflow you already know. Instead of rel

mtmd: fix android build (#26150) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubunt

TL;DR llama.cpp fork with more KV cache quantization features, with all claims supported by benchmarks: KVarN, KV cache precision tail, additional types of standard KV cache (q2_0-q3_1, q6_0, q6_1), a

BREAKING: A Redditor just discovered that shared Claude conversations have been showing up in public search results. The post has 4K upvotes and hundreds of comments, so this is spreading fast. Here's

Running GLM-5.2 (the new glm-dsa arch), Unsloth UD-Q4_K_XL, on a 4-socket Xeon E7-8880 v4 box with 1TB RAM and a single RTX 3060 12GB. ik_llama.cpp, experts on CPU (--cpu-moe), 24 attention layers on

I ran DeepSeek V4 Flash through Claude Code, OpenCode and Pi on my own benchmark, and the quality came out basically the same across all three while the time and tokens spent was wildly different. Cla

Qwen3.6-27B: SFT vs continued pre-training vs RL? I’m interested in adapting Qwen3.6-27B, but I’m increasingly unsure whether conventional SFT/LoRA is the best route if the goal is to add a capability

I'm a software engineer who mainly builds softwaes/applications, and I'm starting to work on machine learning projects. Since ML workloads often require GPUs, I know services like Google Colab and Kag

Kimi K3 will be here on Monday, but how do you know you’re ready to scale it in production? With our latest inference platform updates you can: 1/ Run shadow traffic to see how a new model performs on

Hey r/LocalLLaMA — maintainer here, obviously biased. OpenSmith is an open-source Python tracing tool for LLM pipelines. The idea: drop u/trace on any function, run opensmith ui, get a full local dash

I am setting up codex+ollama+qwen3.6:27b for my hobby coding project on a windows pc with rtx5090 - earlier i tried to setup vllm in Ubuntu container but couldn’t get that to work - now using ollama,

New research from NVIDIA. Does AdamW have a scale ceiling? This work claims yes, and shows where it sits. At batch sizes up to 100M tokens for next-token prediction, SOAP and Muon maintain training st

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

“Pelican on a bicycle” LLM benchmark by @simonw 2024 (https://simonwillison.net/2024/Oct/25/pelicans-on-a-bicycle/) is now “Call of Duty” 2026… Claude Opus 5 one-shotted this game. EVERYTHING you see

https://preview.redd.it/qoogv5m1skfh1.png?width=688&format=png&auto=webp&s=3f06fb28ceec3cd3ab95bcebd0c71374332aac9b Built a simple desktop GUI (Tkinter) that supports multiple local TTS engines (curre

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This part is spot on: > 'Overall, we found that we were over-constraining Claude Code...while these constraints were once needed to avoid worst case scenarios, we have since found we can delete many o

There are a few reasons why problems from International Mathematical Olympiad function as a good benchmark for LLMs: - The problems are new, not included in the training data of any model - Hard math

Qwen3.6-27b is fantastic! It makes me wonder if there's a hard ceiling to smaller sized models. Do you guys think the ceiling of intelligence for smaller models will be constrained by factors like par

Yes, open-source / open-weight models are important for a healthy AI ecosystem. That's how we can verify things, check claims, and keep up outside the closed labs. Plus, it gives us the freedom to run

3 year lurker, now i finally got my server up and running. dont know which model to choose. llama.cpp or vllm, what makes more sense? mainly single user with maybe 2-3 more additional users in family,

Hi everyone, I have a system which I have been using for gaming, R7 5700X, 48GB DDR4, RTX3090 24GB. But I want to use it for Local AI to reduce my reliance on cloud AI providers (mainly usage limits -

Financial Times: A profile of Yang Zhilin, founder of Moonshot AI, which faced early doubts over revenue and model capabilities before its Kimi K3 delivered a “DeepSeek moment” — Known as ‘Yang the ge

Anthropic PBC today rolled out a large language model called Claude Opus 5 to its chatbot service and developer platform. The company says the LLM approaches the output quality of its top-end Mythos 5

Cuda and Vulkan Benchmark: TensorSharp vs. llama.cpp I would like to share my latest open source local Unsloth (GGUF) LLM inference engine and applications. It supports many models from Unsloth, like

I apologize that this question has been asked in various flavors over time, but I couldn't find anything in the posts before that matches the options I have. I have a PC and a mac, both available in m

If you run local agentic coding harnesses (Aider, Claude Code, etc.), prompt evaluation usually eats up most of your execution time. Every turn re-evaluates thousands of identical prefix tokens_system

Claude Opus 5 generated an entire game demo from Scratch, with no external assets used—Matt Shumer posted a video of the AI‑created gameplay on Twitter. The clip, narrated by Shumer as “AI games are g

I understand that the laguna model is either still buggy or potentially benchmaxxed. So I’d like to know for people who really tested, are DS flash or Hy3 really better in your usecase? submitted by /

Hi everyone! Over the past five months I've been working on DKV (DifferentialKV), an open-source project exploring KV-cache compression for long-context local LLM inference. The goal is to reduce KV-c

Hello, I've been learning how to use local LLMs for a year or so on my workstation, using a RTX3090. Current setup : - i5 12400 - 64gb RAM - RTX 3090 - OS : Fedora KDE workstation I'm using LMStudio t

Ha! It did it: 'We introduce BenchBenchBenchBenchBench (BBBBB), an executable benchmark of AI-authored conformance suites for benchmark-evaluation metrics' I really thought it would treat 'now do benc

I’m building the ultimate AI tool vault, but every great collection has a few missing pieces. Note: I will react to every comment AI's currently installed: Qwen3.5-0.8B-UD-Q4_K_XL.gguf(classification)

I’ve spent the past month trying to find the point where an extremely small TTS model stops feeling like a size experiment and starts feeling genuinely useful. Today I’m releasing Inflect v2, with two

like 8 months ago I was tinkering with both and gemini was so much better i went with that. I had a project at work that I needed an AI to sift through a manual and schematic for and no matter what, g