Search: “agents”

Don't upvote, just looking for CPU ram options for running qwen flash next, and maybe larger models (so maybe between 128 and 256gb total ram) with an existing cmp 170hx. Use case is agentic coding so

As a SWE I have ultimate belief that having a irreplaceable subscription on OpenAI and Anthropic models is actually against my beliefs (I'm not fond of becoming a digital slave and being unable to do

What can I do to get more from MTP if anything? It am I sitting at the expected range. Thanks /opt/llama.cpp/build/bin/llama-server --host 10.210.44.126 --port 11434 --model /opt/models/Qwen3.6-35B-A3

Used qwen3.8-27b in Opencode to make this silly mini-game because I'm not sober: ``` We are going to play a game, it will be the Wikipedia game. The Wikipedia game has the following rules: You will ha

It started as a fork of llama-swap, but I have been building it out for myself since then as a convenient tool for all my local AI needs, and by now it has drifted far enough to be its own thing. The

I keep seeing demos of AI agents building scenes in Blender through BlenderMCP, so I tried it myself. I ran both models locally for this and picked the GLM 5.3 family(Q4 quant) because videos of it do

A hallucinated event, Crono awakens in his modest bedroom of 2095... I am using local models since 2025 January. My daily driver is qwen 3.6 35B A3B, which works pretty well for coding tasks, but I al

I'm running Muse Glimmer 30B EXL3-SC 3.00bpw H4, fully resident on my 12GB VRAM GPU at 100K context with Q8_O KV cache. It's a joy to use a dense 30B model at this size and still get 30 tok/s on a VRA

Since I spent the time to figure it out and it is not like it will make me any money ever. I think I'd share with you all what I managed to cobble together. First and foremost here the relevant bits a

TL;DR: Every public low-bit GGUF of this model is secretly ~4.70 bpw. Shim the rows to 256 and it becomes a real 3.07 bpw / 11.77 GiB file that runs 262K context on 16GB. Needs patched llama.cpp — not

Announcement: https://www.tbench.ai/news/terminal-bench-4-0 Leaderboard: https://www.tbench.ai/ Imo the best aspect in their announcement is their focus on rapidly iterating on TerminalBench to keep t

Post rédigé avec l'aide de GLM5.3 flash Contexte : formateur professionnel d'adultes en France, j'utilise des agents LLM au quotidien et le français qu'ils produisent est mon critère n°1, avant les be

I was using UD-Q3_K_XL until now with more than 140000 context. Quality wise it's very good, very few erroneous tool calls. Then I saw many others here reporting good results with IQ3_XXS, so I gave i

https://preview.redd.it/kbsqh6f7molh1.png?width=730&format=png&auto=webp&s=068dbea9a50be634a369d54d8b27b781d020fab3 My experience with Qwen 3.8 for agentic tasks has been phenomenal but I personally f

https://preview.redd.it/i8rjx0ar5mlh1.png?width=2160&format=png&auto=webp&s=c2588bc7b2519ea71b176ca73faf566dfc585496 I wanted to see whether a heavily quantized 27B model running entirely on an older

With hopes of a Qwen3.8-35B-A3B release now mostly dashed, many people including myself are looking at fine-tunes and other variants of Qwen3.6-35B-A3B to run on VRAM-limited hardware. I decided to tr

Granite-4.2-30B is the flagship reasoning model in the Granite 4.2 family. It delivers the strongest performance across reasoning-intensive tasks by leveraging built-in <think>...</think> chain-of-tho

I’ve been going back and forth on this for a week and I can’t settle it, so I’m hoping someone here has hands-on numbers. The two configs (German prices, dealer quote, incl. VAT): Config Price Mac Stu

We have been working on some performance optimisations for Qwen3.8 and other models. The main new feature that we introduced is adaptive speculation for Llama.cpp What is it? MTP and DFlash work well

Hi, Back in late march I begun playing around with Qwen 3.6 27b and found like everyone else that it's notoriously good at tool calls, where every model I tried before just derailed after a few turns

Most YouTube benchmarks only show trivial tasks like generating landing pages or simple Three.js games. Is a local model like Qwen 3.8 27B actually capable of real-world systems programming—such as bu

I can't seem to find a good answer to this, my Hermes agent has access to Firecrawl and some other web scrapers for content extraction, but anyone know of a way to let a local LLM drive a 'real' web b

I used LM Studio Bionic with Qwen 3.8 27B Q3_K_S with 57k context. It took a staggering 63 hours to finish coding. After the first prompt 'Create a beautiful, relaxing flight simulator in a single HTM

Hi everyone! I’m an intern at Icosa, a startup focused on making local AI accessible. We just released the first version of Zeno, our local AI product, and I thought it might be of interest to this co

The benchmark I used is BU bench v1, and the open source harness is Browser Agent. Qwen3.8 27B beat all other affordable or open models I tested. It's insanely good! submitted by /u/pierreb5 [link] [c

Aloha! 🌺Introducing Ornith-1.5, a family of open-source LLMs spanning 9B Dense, 35B MoE, and 397B MoE, trained with self-improving strategies. It achieves state-of-the-art performance among open-sourc

The AI wants to run a higher risk action. Nyx AI stops it here. Nyx AI deliberately invokes a native Windows confirmation before certain higher risk actions can continue. The model cannot approve this

This is invisible with llama.cpp or derivatives, but ninfer helpfully logs the sampler settings on each request and auto-configures the correct ones for the model. Basically, OpenCode will always send

Overview UI-Mate-27B is an open-weight foundation GUI agent for long-horizon work across applications and operating systems. It observes live screenshots, reasons over the visible state, and produces

Guys, could someone smarter than me explain what makes Deepseek Harness so efficient? I run it with local Qwen 3.8 (Q6). I tried Opencode/Openchamber (my favourite so far), Pi agent and Hermes. New Qw

Hey! Like a lot of people here with consumer GPUs (RTX 4060 8GB in my case), I wanted to see if I could use local models for daily coding tasks instead of burning cloud credits on simple boilerplate.

This is a HANDWRITTEN post. I spent way too much time trying to get fast inference on Apple Silicon. This post is for people who want to know what's the latest on running local models on their mac, an

Tested this prompt on the full version (BF16). Though this was a single prompt, I executed using vscode GH copilot extension on agent (allow all) mode and let it do its thing. So there were 54 model t

I am currently building up a local assistant profile on my MacBook Pro M2 with 32gbs. With Claude I am building out this Hermes agent to be my assistant. I am using Qwen3.6 A3B 4bit. We have Frankenst

DeepSeek Harness (dsh) is an open-source agent harness developed by DeepSeek AI. It uses an architecture where everything is a plugin, and is powered by Cordis, whose design is described in A Programm

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

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

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,

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

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

I'm playing with a conversational agent I made using either api/generate or api/chats. In both case I do ask him to not ask follow up question, to not act like an assistant, etc. Either from a system

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 have a classic test for local LLM's. I asked for 8 ball pool game with only one HTML file and Muse Glimmer spend 21k Token(I m using full context so 128k) and only created a 220 lines of HTML and sa

Looking for V100 users to share your config and it's performance. GPU: Tesla V100 PCIE 32Gb Qwen3.6 27B Q4_K_M + Q8_0 MTP 128K context length Pi coding agent llama.cpp model preset: [*] spec-default =

I swear AA is not the bipartisan they so claim. An open source mode (Qwen 3.8 max) was number 1 on the agentic index, then they just so happen to launch 'v4.1.1' of their index in which they just adju

As you all know the model is 2.69B parameters with a 128K context window and purpose-built for multi-step agent workflows. What you are seeing is the Q4_K_M GGUF running on my own inference engine bui