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Is there any way to improve creative writing for Local Models (Qwen)?

I wanted to know if theres anything that can improve creative writing for our Local Models? I specificly am asking for qwen models as they are way better when it comes to researching and writing html

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Local Ai

Least sycophantic modern open LLM?

DGX agent

So Kimi K2 is outdated, and so is GPT OSS 120b. Which of the modern open weights models can boast the least sycophancy? I need this both for creative/research assistant usage (sycophancy led me down b

local-air-localllama

4 Oct 2026

LFM2.5 2.6b vs MiniCPM5 2b

I tried both these models on a few small agentic tasks with tools (i.e. 'What's the weather like today?', etc.). They're both pretty solid at using web search to find answers despite being small model

LLM Inference Dashboard

Working on a resource dashboard, rich logs, lightweight 64mb cap, all local, scales on network API endpoints via collector, supports multiple engines (llama, strata, custom cuda engines, unsloth, LMS)

Need maybe say 'Use llama.cpp'

So I tried that miracle engine everyone is talking about. Asked the IQ3_S model to express its opinion on a post from this sub to measure the tps on a long-ish generation: Can you help with the follow

Poor People Vulkan GPUs list

Help with this list. Give me your recommendation on 'not supported anymore' GPUs. Looking for budget and Vulkan friendly options. Most of the GPU are not supported by latest CUDA / ROCm. Often with so

poorman inference engine for 16GB GPU and 35B moe Qwen 3.6for coding

I forked llama.cpp's server into AgrillaMoE, a dedicated build for Qwen3.6-35B-A3B (~A4B) with Unsloth quants. On a (vant.ai) rented V100 16GB with the 2-bit UD-Q2_K_XL quant it generates at ~57-60 to

Qwen3.5 arch implementation in FPGA fabric for 9B/27B INT4 models on relatively cheap eBay mining hardware

I've been wanting to test out LLM inference on FPGAs for a while now, but didn't have a big reason too since there were no frontier class models at 9B-27B scale (3.6 27B was great still but it didn't

The curse of 64GB system RAM

Not a bot. Not a Strata shill. Just sharing my experience. So, I have an R9700 in my machine, plus an RTX 5060 Ti, and 64GB DDR5 system RAM. Overall, not a bad setup. Anyway, I mainly run a daily driv

Update #4: Post training yandex/AliceAI-80B-A3B [instruct!] from scratch

Last update for those following: https://www.reddit.com/r/LocalLLaMA/comments/1wvyc3e/update_3_post_training_yandexaliceai80ba3b/ Project in a sentence: An instruct finetune of ALiceAI-80B-A3B-Base ca

Upgrading from 1xR9700 to 2xR9700. Thoughts on build before buying?

Currently have a R9700 build with 64GB RAM. Want to get a second one for both speed and the ability to run Qwen3.8-Flash-Next at Q4/higher quants of 3.8 27B. Looking for thoughts or any improvements b

Hardware

Anyone else finding it harder to justify a new GPU upgrade lately?

I have been running the same midrange card for a couple of years now, and every model release sends me back to the price sheets. The performance gains are real but they keep landing next to price tags

hardwarer-localllama

Anyone sitting on a lot of slow system memory and a modest GPU.. try Strata + Qwen3.8 Next.

IQ3_XXS weights are just under 80GB and my slowww DDR4+7900XTX is stabilizing around 45-70/s (sometimes higher while coding depending on mtp). Looking online I'm seeing similar results for users with

Anyone using a local AI meeting notes setup instead of Fathom?

Meeting notes are one of the last parts of my workflow that still depend heavily on cloud tools. I've used Fathom and lately Bluedot. Bluedot works well for me because there's no meeting bot and I get

Anyworld, a self-hosted multiplayer text RPG where a local LLM is the Dungeon Master

Hey everyone, I’ve been working on a game called Anyworld. It’s a browser-based multiplayer (single player also supported) text adventure inspired by the early days of AI Dungeon, especially its brows

Agents

Arex-2 vs swift vs qwen3.8 27b

Hi, Im gonna go all in on this, best finetune iv got my hands on for agentic coding, period. Test it yourself, im not gonna give you any benchmarks or evaluations, use your own tests and pracices, giv

agentsr-localllama

Come let your LLMs play World of Warcraft

I hosted my own world of warcraft private server then built a client that you can play in the browser on PC or mobile at https://jankcraft.xyz/ for free. Afterwards, I created a custom MCP and agent h

Dual Radeon MI50 benchmarks

Still don't have a good cooling solution, but here are few benchmarks. I lowered the power limit (TDP) to 145 watts each. I changed the firmware on one MI50 to activate the miniDP port. Did have to us

I built a code knowledge graph tool that's actually MIT licensed (fully local, no cloud)

So this is maybe a niche problem, but at my job I work on a huge Python codebase and every time I change some shared function I'm basically playing roulette. grep tells who mentions it in the code bas

I built Ninfer 4080 for 16GB class GPUs

Hi everyone, TL/DR I created NInfer 4080 to run ISTA-DASLab-Qwen-3.8-27B-GSQ at 100k context on an RTX 4080 16GB GPU using way more of the hardware capabilities (max overall: 2720 tok/s prefill, 262 t

I tried building a small RAG search node for Qwen3.8 27B using a fake AliExpress Mini PC... and Intel sent me back to 2018.

I love Qwen3.8 27B so much that I decided to show my gratitude to the Alibaba ecosystem by building a dedicated RAG/search node using a cheap Mini PC from AliExpress. Turns out, my ecosystem loyalty g

LiquidAI/LFM2.5-Encoder 250M/350M

LFM2.5-Encoder-350M is a multilingual bidirectional encoder built on the LFM2 architecture — a larger encoder for maximum downstream quality. It is a masked language model with full bidirectional atte

mlsubgen — subtitles in 45 languages for your videos, entirely on your own machine

Full disclaimer: I've leaned heavily on Fable to develop this, but I've tested it thoroughly on my own library for a couple of months before putting it on GitHub. I live in Thailand, and it started as

Qwen3.8-Flash-Next 177B running at 11–15 tok/s on a single RTX 5070 12GB + 32GB RAM DDR4

Benchmarking an LLM here with a NVIDIA RTX 5070 12 GB VRAM here I had been working on a llama.cpp based expert streaming setup for Qwen3.8-Flash-Next 177B (UD-IQ3_XXS) on Windows. Benchmark is about 1

Replacing vector databases with SQLite and SIMD hypervectors in under 1.2GB VRAM (Hillock)

Disclosure: I am the creator of this project. After days of lurking and building up enough karma, I can finally post here. Every time I tried running local RAG on my own machine, I hit the exact same

Running Qwen3.8 Flash Next 176B on a 16GB RTX 3080 Laptop + 32GB RAM + SSD

I wanted to see how far I could push a fairly ordinary laptop with a huge MoE model. Turns out, Qwen3.8 Flash Next 176B can run on: RTX 3080 Laptop — 16GB VRAM 32GB system RAM SSD No 128GB/256GB RAM w

Sopro V2 Turbo 2610: cleaner cloned voices, same 120M model, same CPU speed

Follow-up to last month's post. One of the main issues people ran into was roughness or break-up on some cloned voices. 2610 is an interim update focused mostly on improving that. Reduced roughness an

The fastest interference engine for RTX5090 and Qwen3.8 27B. Twice as fast as ninfer. 500+ t/s single coding, 2000+t/s up to 12 agents at the same time with 800k context. Smart VRAM-RAM-DISC Cache management, Loop Guard, Nice UI etc.

Hi guys, I am pretty happy to announce MegaCapybara. Purpose build engine for RTX5090 that is focused on Qwen3.8 27B (more will come later). GITHUB (Engine) HUGGINGFACE (weights) Why ? 1. It beats Nin

The Rise of Overfit Inference Engines

There seems to be a whole category of extremely narrow inference runtimes appearing: Strata, ninfer, DwarfStar, Splash, llamAmpere, gufo, etc. They deliberately give up the thing llama.cpp/vLLM are gr

The ultimate guide to multi-harness RL

Hi folks, it's Lewis here from the post-training team at Hugging Face. We've been exploring how to train open models in different coding harnesses and wrote up a looong guide on how we solved this usi

Two ~300B MoE models, each on ONE 128 GB mini PC (AMD Strix Halo): GLM-5.3-Flash at ~580 tok/s prefill, MiMo-V2.6-Flash up to 44 tok/s decode. EXL3 weights + open ROCm engine

We built an engine, Kyojin, on top of ExLlamaV3 for Strix Halo (gfx1151, ROCm), and packed two 300B-class MoE models so each fits one 128 GB machine. First release, all measured on Ryzen AI Max+ 395.

Two local Qwen ( 3.8 27b unsloth Q6 and Qwen flash next strata coder ) models vs Claude Opus 4.6 on the same 3 coding tasks. One of them tied it. Not here to start a fight, just sharing numbers

Innanzitutto, due cose per evitare fraintendimenti. Non sto cercando di sostenere che un modello o un'azienda siano migliori. Non ho alcun interesse personale in nessuno di essi. Volevo solo verificar

Unitree just dropped UnifoLM-WLA-1.0 — a single 6B model that does 64 whole-body + tabletop tasks on a real humanoid

https://unigen-x.github.io/unifolm-wla.github.io/ Unitree Robotics released UnifoLM-WLA-1.0, their new general-purpose humanoid foundation model. Key points: • 6B parameters • Trained on ~2,500 hours

Anyone wonder why americans lag so far behind in the open LLM market?

I mean , DeepSeek, Kimi, GLM, MiniMax -- the list of Chinese LLM's is such a long freaking list. As American's -- why don't we feel .. a little funny .. about being so far behind? Chinese entrepreneur

best <40B alternatives to Qwen/Deepseek for (1) Coding (2) Long document QA test

Due to some reasons, Qwen/Deepseek Chinese models are NOT allowed in the my workplace. So, what local models, do you think, is the best alternatives to Qwen/Deepseek for (1) Coding (2) Long document Q

CalDec v1 - Fully Open Decision Model for Personal Assistants

Somebody just released a fully open-source, open-weights decision model that beats Jev! Just kidding, it's me and I this is my first time releasing a public model, recipe and dataset so I am looking f

I made my iPhone a second GPU for my 24 GB MacBook: Qwen 3.8 27B prefills 29–44% faster & my holds part of the CTX window.

**DISCLAIMER** THE PREFILLING TPS SHOWN ON THE PHONE IS COMPUTED ONLY FOR THE LAYERS IT HOLDS. ALREADY FIXING IT TO SHOW END-TO-END PREFILL RATE. NUMBERS BELOW ARE ACCURATE FOR E2E PREFILL RATE. Every

I made my own cybersecurity benchmark and ran Qwen3.8 27B, here's how a local model actually does at hacking

Hey local AI community, I've been working on this for a while and finally feel ok sharing it. It's a cyber benchmark where the model gets a shell in an isolated docker box and has to find the exact fl

I spent 3 weeks testing local Qwen3.8 on the new low-latency SGLang/vLLM recipes: DFlash2 2.8x. Builds: RadixArk + Inferact 27B NVFP4, 27B BF16, orcarouter 27B Uncensored, Flash-Next NVFP4

Hey guys, Last time I tested Qwen3.8-Flash-Next on its own. This time I put three Qwen3.8 checkpoints through the same 10 tests on the same RTX PRO 6000: RadixArk/Qwen3.8-27B-NVFP4 (dense) orcarouter/

Inference Engineering for Dummies

Hi all! I am a former SWE who has recently transitioned into inference engineering. I launched a side hustle a few months back and I've just taken it full time due to excessive demand. Its been such a

Applications

I've ended up with an AI lab in a public community college. What should we actually be teaching?

Looking for some ideas from people who know a lot more about this than I do. We've got funding for a small AI lab in a public further education college in Ireland (roughly community college in the US)

applicationsr-localllama

llama, server: add /v1/systemone API (models: laya, julia-1, lev, openjev, kev) by ngxson · Pull Request #29818 · ggml-org/llama.cpp

Now you can jev without jev https://huggingface.co/ggml-org/OpenJev-GGUF https://huggingface.co/ggml-org/lev-GGUF https://huggingface.co/ggml-org/Julia-1-GGUF https://huggingface.co/ggml-org/Laya-GGUF

microsoft/FrogNano-4B-2609 · Hugging Face

An agentic model from Microsoft for the GPU poor https://huggingface.co/bartowski/FrogNano-4B-2609-GGUF FrogNano is derived from Qwen/Qwen3.5-4B, a general-purpose post-trained model designed for lang

Model weight inferencing

I have 4050 6gb gpu, 24 gb ram which model should i choose to run i need speed. i try qwen 3.8 27b and feel too slow tried from onslot studio. I have heard of weight inferencing does it helpful what s

New Architecture from Percepta: Spotlight — separating intelligence from memory, allowing knowledge and skills to grow without changing the model's weights.

https://www.percepta.ai/blog/can-llms-grow-their-own-capabilities https://www.percepta.ai/blog/spotlight-memory 'Our new architecture, Spotlight, replaces attention with a memory that escapes this tra

New benchmark on LMs fixing bugs before users run into them

Hi! This is a new benchmark that I created together with other researchers at Meta, Stanford, Harvard, UW. Most benchmarks these days seem to test models to just fix a bug that I as a user already enc

Peacebell - a from-scratch small language model

I've been using my free time during weeknights and weekends for the last 11 months working on and refining a small domain-specific language model. It specializes on information about World War II. The

Qwen3.8-27B-Humanlike-Chat 2.0: texts like a human, now with tool calls and better instruction following

Last month I posted a Qwen3.8-27B LoRA that makes it talk like a person instead of an assistant. It got a lot more attention than I expected: 700+ upvotes, 248 comments and 44k downloads since. I read