model releases

Hidden Reasoning from Claude and GPT are Decoded, and it is interesting

Yesteday a paper showed a gap that allows to see 100% of the reasoning tokens form ALL Claude and GPT models Stealing Reasoning Traces from Proprietary LLM APIs. check it out, they have published lots

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Idea for a deepseek-v4-flash-0731 backed automated research workflow to be leveraged via qwen3.6/3.8 27b for difficult tasks that require highly technical, not easy to find information.

DGX agent

Sometimes you have tasks that are outside of your expertise and the idea is this workflow automation could be leveraged to manage to have local AI figure it out using research from his workflow gather

model-releasesr-localllama

12 Aug 2026

New Muse-Glimmer-30B SoTA Quants - hopefully a new lineup :)

Hey Folks, I've been making quants for a while - recently I took a short break to get into hardcore research (submitted my first EMNLP paper during it!). Along the way, I built up a little arsenal of

Qwen 3.8 2.4T is out , no 27b today RIP.

i was hoping they would release both today but the big one just dropped now , and it says its been listed since 5 hours ago on huggingface. I guess the real date for 27b is https://modelscope.cn/model

Tested Nemotron 3.5 Lightning locally on coding, Hermes Agent and agentic work

Ran the model with quants (Q5) and MTP by bartowski with llama.cpp server. It takes ~24GB ram running on M5 Pro with 48GB at about 65t/s. On some tasks it was quite the overthinker. Overall, the quali

What unique, custom QOL upgrades have you given your local agents?

Warning: Kinda long post. If you don't like reading, please skip for your own sanity. Also, I've got nothing to sell, just a tinkerer, so I just want to share ideas and learn from you guys too. When I

1 Day in and I feel okay saying Muse-Glimmer-30B finally beats 3.6-27B for the size in some use-cases

A few things right off the bat: it reasons very efficiently. Like Grok 4.5 levels of efficient thinking it quantizes very well. My first few tests with iq3_xxs were better than Qwen/Gemma behaved at t

10 year garbage card for local llms

Hello everyone! I like dumb things. I like working with weak computers and microcontrollers. I like the simplicity and low electricity usage. Simply put, the efficiency of a 'dumb' PC. The first tim

12GB VRAM gang, what's our plan?

Seems like we're limited to qwen finetuned MoEs for now. Looking at the current landscape - focus seems to be on dense models (muse glimmer 30b, qwen 3.8 27b) for smaller setups. Is upgrading to 24GB

Anyone Using (Koreas) 'Solar Open 2' (250B, 15B) Model?

I just heard of this model. Seems to be a competitor to DeepSeek V4 Flash. About the same size and active parameters. Anyone tested it compared to V4 Flash? Link: https://huggingface.co/upstage/Solar-

Can Gemma and Qwen models catch hallucinations by looking at their own logprobs?

Hi! I'm really obsessed with LLM hallucinations for the last 6 days 😭 I started by designing system prompts to attack hallucinations but failed, obviously. Now I tried reading logprobs and... I think

DeepSeek-V4-Flash-0731 (284B MoE) at 75 tok/s on 2× DGX Spark — full recipe, 11 gotchas, reboot-proof cluster, Codex CLI integration

Spent two nights getting deepseek-ai/DeepSeek-V4-Flash-0731 (284B MoE, 13B active, native FP4/FP8, 1M context) running production-grade on two DGX Sparks connected by one QSFP DAC cable. Everything —

DeepSeek V4 Flash 0731 at 27+ t/s decode on Strix Halo — Vulkan + DSpark full guide

Been benchmarking DSv4 Flash 0731 on a Flow Z13 (Ryzen AI MAX+ 395, Radeon 8060S / gfx1151, 128GB LPDDR5X) for the past week. Figured I'd share what actually works and what doesn't — there are a lot o

DeepSeek-V4-Flash acting as my Linux sysadmin

I'm very happy with some Linux admin tasks I'm throwing at a locally running DeepSeek. My request was simple, check why 'samples' folder is taking more and more space on one of the machines on my LAN,

I built a weird, low-power llama.cpp server using an Intel N100 + RTX 5060Ti

Everything started with the sudden death of my old ASRock J1900. While looking for the perfect ITX replacement, I stumbled upon the Chinese CW-NAS-ADLN-K motherboard, which looked perfect on paper: In

I gave DeepSeek V4 Flash basic vision by training a 40M connector on 100K examples

I wanted to find out whether a huge text-only MoE could be given basic vision without retraining the language model itself. The short answer is yes. I froze DeepSeek V4 Flash and a 417M-parameter Moon

I put Gemma 4 E4B and E2B into an e-reader so I can ask my weird questions and share my thoughts in private directly in app.

Here's how it works in the app: Framework: Runs on LiteRT-LM (like Google's AI Edge). Models: Downloads either the E2B (~2.5 GB) or E4B (~3.6 GB) INT4 quantized models directly from ungated litert-com

I ran Muse Glimmer @ 1M context - All tests passed.

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

I tested the CMP170HX

Lots of rumor and misinfo bouncing around, so I put some of these old mining cards to the test. I used 4 of the 8GB cards, set to 64GB each. Lots of models fit entirely on a single card, and you can a

Introducing Unsloth Desktop app

Hi LocalLlama, we're super excited to release Unsloth Desktop today! 🦥 It's the first desktop app that enables you to run and train models locally. Open-source. Available on Mac, Windows, and Linux Su

Llama-CPP Parallel Agents --> fine for decode, but one agent's prefill will grind all other agents to a halt

Testing with 3-5 agents. Decode performance is superb, however if one performs a web search and needs to process a few thousand tokens, ALL other agents will grind to a halt: I've tried tuning a littl

[llama.cpp PR #26608] Ling-3.0 support (unmerged)

aetherbird has done some great work getting Ling-3.0 to work in llama.cpp. The architecture is generally identical to deepseekv2. I recently added a microscopic 40 line PR to his that adds support for

Local Benchmark : Muse Glimmer 30B vs Qwen 3.6 27B vs Gemma4 31B (and many other models and finetunes)

Needs a lot of requests compared to Qwen (almost twice) and Gemma (almost x3). Final score is fine, even though it is 'not a coding model' https://wonderrico.github.io/local_llm_benchmark/benchmark-ma

Luth-2: New State-of-the-Art French Small Language Models

Hey everyone, Today we release Luth-2-0.8B and Luth2-2-2B, two non-reasoning models that set a new state of the art for French across a wide variety of tasks for their size 🚀 A few notable scores on F

Muse-Glimmer 30B Hits ~280 t/s in Real Production Coding

These numbers were captured during a real feature implementation task in Next.js and Nest.js (adding a theme switching system across components). The structural predictability of UI/state refactoring

Observations on Muse-Glimmer reasoning traces being noticeably different from qwen / gemma models and questions for you guys

Just downloaded the model, UD-Q5_K_XL quant, asked it to generate a long story to test out reasoning and speed with dflash (super fast btw, ~ 90 to 160 tok/s on a 5090 depending on task) and was surpr

OpenAI just launched a cybersecurity model that answers 95% of advanced threat queries. And Meta put a frontier model on your laptop. Same day.

Something happened today that I think most people are going to miss because there are two separate stories and neither one is getting the full picture. OpenAI expanded Daybreak. If you haven't heard o

Psychological methods really work. Has anyone tried to encouraging and instilling confidence to GPT?

Oddly enough, encouragement actually influences the performance of not only Claude but also GPT and other AIs. While Claude was working on a complex problem related to the Riemann Hypothesis, the Anth

Tested in Coding: BF16 Muse Glimmer vs BF16 Qwen3.6 27B

I'm guessing that many people have been waiting for this comparison. For clarity, both models are running at full FP16 KV-cache. Due to VRAM limitations, Muse Glimmer is running full 262,144 context,

The small open weight models are scarier in AI development

Imagine if your everyday laptop could run an AI model smart enough to take care of 90% of your work—totally private, lightning fast, and completely free of monthly fees. That is the exact tipping poin

Today it was apparently my turn

I’ve been using ChatGPT for about two months now after giving up on Claude and Gemini and I became a true zealot. I’m now using Pro, and despite having the personality, set to default, never had any p

Toy project: a chat title model that fits in 5 MiB of ram

Not even sure if I'm allowed to post this, what with the 'completely/primarily LLM generated copy' rule (the post itself is fine, but the repo/model I'm sharing definitely is, whoops) and the whole li

We quantized DeepSeek V4 0731 and benchmarked it against popular quants on 8× RTX 5090

We converted the model from the original safetensors and found two issues. The first one made our quantization fail several times, the second one does not fail at all, it just quietly ruins the base 1

1M context with 17 GB model in 24 GB VRAM: 'for the first time I was able to load a context of almost 1M tokens and extract 7 needles from various parts of the text'

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

Achievable 253 t/s - unsloth/Muse Glimmer 30B UD-Q5_K_M on a 5090

Benchmarked Muse Glimmer 30B on my RTX 5090 (32GB), 262k context, UD-Q5_K_M + dflash-kquant + mmproj. Workload Stock master + DFlash ngram-simple PR #26842 + DFlash Code patch 78 t/s 57 t/s 220-253 t/

Best open-source harness like Claude Code?

Avid claude code user here looking to do equivalent things with local models. Just want to plug in something like Qwen and have the interface be 1:1 with claude code. Any suggestion? submitted by /u/N

Chat UIs with native audio input for multimodal models?

I've been running Gemma 4 E4B with oMLX and I can't find any chat interfaces that directly send the audio file to the model instead of running the audio through a separate STT layer. I can confirm the

Comparing how Cline, Kilo, and Qwen Code handle long-task context/state (and why context loops keep happening)

I've been comparing Cline / Kilo / Qwen Code lately since they all handle long-task state differently. Cline: has Focus Chain, a markdown file kept outside the conversation that gets reinjected on a c

DeepSeek V4 Flash 0731 is the ‘killer app’ that is going to sell A LOT of DGX Sparks

Having a ‘Killer Application’ that everyone wants to use helps sell hardware, plain and simple. DeepSeek V4 Flash 0731 isn’t an app of course, but I think it’s going to be the major catalyst for getti

GPT 5.6 Sol High and X-High (Web Chat) feels severely nerfed since 08/06/2026 update

GPT-5.6 Sol High and X-High (Web Chat) feels severely nerfed since 08/06/2026 update (https://openai.com/index/improving-gpt-5-6-sol-in-chatgpt) I use it for a pretty complex Unreal Engine 5 project (

I asked OPUS 5 to make a video about what it's like to be an LLM

Full prompt I gave to Claude Opus 5: can you use whatever resources you like, and python, to generate a short 'youtube poop' video and render it using ffmpeg ? can you put more of a personal spin on i

I compared GGUF quants of Qwen3.6 27B to NVFP4, AWQ, AutoRound, and FP8

There's an interactive chart and some extra data in the blog post if you're interested. There are plenty of KL-divergence benchmarks for GGUF models, but most of them compare one GGUF quant against an

I trained a 1B-parameter LLM from scratch on 20B tokens for about $200

A few months ago, I had the idea of making a LLM from scratch as a personal project (for learning and partly for improving my resume). Since I learned a lot from other posts on here over the past year

inclusionAI/Ling-3.0-tiny · 8B A1.3B MoE· Hugging Face

Looks like the Ling team open weighted a much smaller version of the Ling-3.0-flash they open weighted a few days ago. It's 8B params with 1.3B active, and seems to fall between the 4B and 8-12B Qwen

Introducing Muse Glimmer: an open-weight model optimized for always-on local agent workflows

Hi r/LocalLLaMA 👋 Today we’re excited to release Muse Glimmer, a 30B open-weight model built specifically for local agent workflows. We’re releasing the weights to the community under a permissive Apa

Most Civitai 10$ checkpoint's are scams. Don't fall for it.

model-releasesr-stablediffusion

If you read -> huge claims + AI like generated presentation + no negative comment AND '$10 to download on my patreon/whatever' = they're scammers. Period. 1- Anyone leaving a negative comment or tiny

Motif-Technologies/Motif-3 official realese

Motif-Technologies is one of the tech company participated South Korea's AI Foundation Model project.(독파모) Upstage(Solar Series), LG AI Research(EXAONE Series), and SKT(A.X Series) are the competitors

Muse Glimmer ACTUALLY fits on a single RTX 3090

I did some testing this morning, and I was surprised to find that Muse Glimmer actually comfortably fits on a single RTX 3090 with full context + DFlash + mmproj at Q4_K_XL, unlike Qwen3.6-27B and Gem