Search: “r-localllama”

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

Setup for one long agentic coding session (tools + vision) on a single 5090: Qwen3.8-27B RVN Heretic (ARA abliterated) at Q6_K, 159744 context, 1 slot, q8_0 K/V, DFlash2 speculative decoding, vision p

Achieved steady 15 tokens/second output and 100-120 promp processing per second with 12GB GPU (RTX 5070 SFF) with Qwen3.8-Flash-Next-GSQ-RCO-GGUF at 3 bpw (IQ3_XXS which maches BF16 on AIME25). Around

At last my small pebble of contribution has been merged. Thanks to CISC. Support for the model DFM Mimir 1B gguf here: https://huggingface.co/noctrex/DFM-Mimir additional optimization will be added in

https://preview.redd.it/e5wn8eyh7vph1.png?width=1080&format=png&auto=webp&s=5faeec866acc9e8eca8dc9e84a6661bee7bcf954 https://preview.redd.it/8ov5gl8j7vph1.png?width=1080&format=png&auto=webp&s=440320c

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

Follow up post of https://www.reddit.com/r/LocalLLaMA/s/nGMyKswrch. Thanks everyone who replied. I didn't change the specs. Might be loosing some of the memory bandwidth but will scale in future if I

What taste in humor do LLMs have? Do LLMs like certain types of jokes? Do they have strong opinions? Our report: LLMs have some overlap in humor taste, about 3/4 agreement but it ranges from 54.9% to

With the recent Nvidia Huggingface acquisition and frontier AI labs screaming about safety and putting guardrails everywhere, I think it's important that we have local models that aren't affected by a

I've been working on something for this community (and other self-hosted communities) for almost 5 months now. Personally, I would grade it as probably the closest open source equivalent of Manus and

https://reddit.com/link/1wi2zuc/video/rs1a4sx92xph1/player Hey everyone, I’ve been experimenting with local LLMs on Intel hardware, and I wanted a hyper-optimized, standalone CLI tool that didn't rely

I'm pretty sure it can be done with any model based on qwen4exp, which Qwen's next local models will be based on. You can use a quant that barely fits in VRAM and still run at the model's maximum cont

I implemented expert lookahead, a trick that gets a 10%+ performance improvement on MoE models running on low-memory macs (Qwen 3.8 flash in this case) while using expert-offloading/ssd-streaming (usi

So I would like to run qwen 3.8 27b locally for my ai agents, maybe even in parallel with other small LLMs (such as qwen3.5 9b, oss 20b etc..). What is the best hardware to do this? Not a video card b

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

example: https://huggingface.co/audnai/penclaw-GLM-5.3-abliterated-for-offensive-cyber. edit: good context for why this was done here https://www.reddit.com/r/LocalLLaMA/comments/1whbvpx/comment/pa16g

Disclaimer: no AI was used whatsoever to write this post Cautionary tale about chasing cheap tokens. exposé: https://kendell.dev/blog/crofaifalse/ reaction by nahcrof, announcing the shutdown of the s

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

I liked DeepSeek V4.1 Flash as an agent model but at 16 t/s on ds4 it was painful to sit through a real turn. It seemed some redditors and m3 ultra owners appreciated my glm 53 flash optimizations, so

One thing I love about both Qwen 3.8 27B and Flash Next is the effort it puts in to make a good product. It is the opposite of a lazy model and 'good enough' is never what aims to accomplish. It goes

I have tried using qwen 3.6 and 3.8 q5 64k context and I'm just not getting the experience everyone here seems to get. I'm using open code and it will loop, forget what it's doing, and mess up the ui.

After two days of fighting with benchmarking infrastructure, I finally benchmarked IFM/K2-Horizon-7B on 16GB VRAM. TL/DR: it works, but far behind Qwen-3.8-27B. Setup I compared 3 models - Qwen3.8-27B

I have a custom fork of llama.cpp designed around the ampere architecture specifically (though many of the upgrades also translate to faster performance of blackwell + lovelace). The recommended confi

For years, the JVM has watched the AI revolution from the bench. Every model, AI framework, every breakthrough, built with/for Python. jinfer is an inference engine built for the JVM from first princi

I am a beginner, Took a while to get started, get everything right. This setup is native not container. Still not sure if I did this right, or if I can tune this more. Environment=HF_HUB_OFFLINE=1 Env

As you know, Qwen3.8 27B has built-in support for embeddings. It gives pretty decent results in llama.cpp with the following parameters: --embedding --pooling mean The problem is that enabling embeddi

Hey everyone! We’re launching Voice Acting Arena with LAION x TTS Arena, and we’d love your help trying it out 🙂 You’ll hear two anonymous AI performances of the same scene and script, then compare th

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

Personally, I think the whole 'AI (LLMs) is going to take over' is just BS marketing. They've been pushing this doom-and-gloom since 2019, and we all know the models back then were nowhere near as cap

I reallise this is not exactly a locally hostable model, at least definitly not on my hardware. But I figured some of you might have some insight on this. I run an agentic coding benchmark. The model

I had all the data saved from AA's v4.1 index, so when they upgraded it in the wake of Astra's release, I could actually generate a before/after comparison. All intelligence and price per task are sam

I know I know, it's great, we know. I've been working on tweaking inference engines for a week now and it's been one shotting most of my vague prompts without any issues. It will even write tests and

So we’re seeing how Anthropic and OpenAI are gunning for regulations. I think most of us realize that this is a ploy for them to achieve regulatory capture, thus securing their moat and kicking out op

Edit: Spelled Baseten not Base-10 Full episode of this available at https://www.youtube.com/watch?v=PrSf7IOYu-I It's interesting to see how Dwarkesh has had to come around to the evidence that we are

Do you have something you'd like to share with the r/LocalLLaMA community. This is the place for it! Recommendation on presentation: Please share plain english description of what your project does an

Note - This is translated from the actual blog link right at the bottom. A few days ago, DeepSeek v4.1 was released. It raised the ability of small models to a new level. AI is improving much faster t

https://artificialanalysis.ai/articles/artificial-analysis-intelligence-index-v4-3 AA shipped a new benchmark last week as part of the Intelligence Index v4.3 update — a brand-new private eval that re

From initial testing it seems pretty solid so far. Asked it to compile the latest llama.cpp for CUDA and its doing well so far. If this thing holds up to its score then its SHOCKINGLY good for its siz

I'm like you guys and am constantly experimenting with new models, seeing what they're all good at, how I can make use of them for certain projects and goals. I've been using Qwen 3.8 27b for minor co

Model: https://huggingface.co/internlm/Intern-S2-397B Collection: https://huggingface.co/collections/internlm/intern-s2 From Intern Large Models on 𝕏: https://x.com/intern_lm/status/209942518458737097

I'm currently using Qwen3.5 4B as the brain of my local AI assistant because my hardware is relatively limited. One thing I really like about it is the speed. On my system, I'm getting around 40–50 to

The full K2 Horizon lineup is out on Artificial Analysis. The AA intelligence vs. parameters plots show that - 0.9B and 375B are bad - 3.7B and 7B are SOTA - 36B A4B is SOTA for hardware with poor mem

Pushed out this new update, hopefully decreases the instances of crashes. I torture tested this one for ~12 hours after my fixes and found no instability. Q4 K XL needs more fine tuning, which I will

Hello Reddit. Posting this for fun. I thought it was a lonely and silly journey to set up Qwen 3.8 Next on a system that doesn't really run it properly—it was a challenge that might help the community

Now that the dust has settled a bit - here's a write-up on running Qwen3.8-Flash-Next (125B-A6B MoE + 51B n-gram table) on relatively middle-tier hardware (RTX 4070 12GB + 64GB DDR5-5600 + Gen4 NVMe o

Hi everybody, we post-trained Qwen 3.8 27B to be more efficient by figuring out which tokens were linked to overthinking and penalizing them without 'attacking' the reasoning length directly then fixe