Which agent harness do you use and why?
I see a new one being launched every few days... How do these new harnesses compare to claude code, pi etc. has anyone switched from these? which harness to prefer and why submitted by /u/Background-J
Knowledge catalogue
I see a new one being launched every few days... How do these new harnesses compare to claude code, pi etc. has anyone switched from these? which harness to prefer and why submitted by /u/Background-J
I built a denser GGUF format for ternary models: Q2_B3 / “B3S” If you're running a ternary model like BitNet-b1.58 or Ternary-Bonsai, the weights are already restricted to -1, 0, or +1 times a block s
I always heard that cryptocurrency mining can damage a GPU ( it's maybe wrong) so how much long term inference is damaging a GPU? I mean servers are made for 25/7 operation... Gaming GPUs not so sure?
Hey everyone, been a while! https://huggingface.co/TheDrummer/Artemis-31B-v1.1 https://huggingface.co/TheDrummer/Artemis-31B-v1 A few months ago, Gemma graced us with models that served as a much need
A month ago I posted about Gemma sometimes solving the reasoning problems inside my translation data instead of translating them. A few people suggested two very fixes, which is to use a proper transl
How good/bad is it against comparable MoEs the same size? How does it compare against Qwen 3.6 35BA3B? Since we don't have 3.8 35B this seems like an upgrade if we look at some benchmarks like termina
Let's say I have 2x 16Gb GPUs and I want to run Qwen3.8 27B. Monitor is ran by the integrated GPU so both 16Gb GPUs are almost fully free. I load the UD-Q4_K_S on one card at 15.4Gb. I then load the c
I am getting into developing a RAG setup, for getting information out of existing documents, new document ingestion, web searches, and good visuals. I am planning to use it for, alongside the regular
We all want more tokens per second but I keep seeing confusion on what to expect for given hardware. For the decoding phase (TG/s) to produce one token all the model weights and KV cache needs to be r
After Qwen3.8 27B came out, I decided to benchmark the models that could fit in my GPU (RTX 5080) on my actual code (C code), the results were not completely unexpected but some quants were definitely
This epyc server I am using twelve cards with 64 GB memory, plus 256GB ram. Looking at the most capable models in open source, GLM 5.3 seems to be the only option, but with Astra releasing it will lik
It performs well across visual perception, STEM reasoning, document intelligence, multimodal agent tasks, frontend coding, and medical report interpretation. submitted by /u/niacolhealth [link] [comme
This is nothing impressive but, i had so much fun i wanted to share my experience with this model. (yes this post is written by human) I made an FPS with local Q4_K_XL 3.8 Flash Next (256k context) (i
You know that thing where you just throw a task at a frontier model and not have to supervise it worrying of it going off course? Qwen3.8-27b has officially gotten me to that point for local work. He
I've got an refurb Dell R740 running Proxmox that I put a Tesla T4 in, mainly to run some CTC local transcription work, but thought it would be fun to try DS4 when it came out, and it was appalling at
Claude Code is absolutely horrendous at managing docx XML structures and it's obscene that the best these frontier companies can do are skills. Skills are prone to breaking, burn tokens (hello busines
Being a person obsessed with testing new models that come out, times are really insane for me. Tested different kinds of TTS and voice cloning models but none of them gets it right in terms of emotion
This is a follow-up to my post from yesterday (17 -> 25-29 t/s with the expert cache PR). Same box: 2x RTX 3090 on PCIe 3.0, dual Xeon E5-2696 v4, 188 GB DDR4-2133 LRDIMM, llama.cpp, full 261k context
I was using: weights https://huggingface.co/RadixArk/Qwen3.8-Flash-Next-NVFP4 with the optimized SGLANG (patched) from https://old.reddit.com/r/BlackwellPerformance/comments/1w04xb7/qwen38_flashnext_o
I'm one of the developers. We said in August it would go open source in September and it did last night. MIT or Apache-2.0, pick one. The repo you see is our internal repo, kernels included, so from n
Github link: https://github.com/thatblend/LLMPSP I wanted to see what the PSP can theoretically handle and I got my answer - a 90M model is about the max it can do without atrocious inference speeds.
Ling-3.0-flash-Fin is the first finance-enhanced model in the Ant Ling family. Developed by Ant Group with leading financial institutions and domain experts, it extends Ling 3.0 flash through continue
I am working with multi-language content and have so far been useing gemma4:e2b for general work. Today I tried it with Russian for the first time and the result was not great. Gemma4:e4b was better,
I've been building Arcon around Qwen3-4B + LoRA. Instead of just making it a chatbot, I'm experimenting with persistent memory, personality/mood, internal state, tools, and eventually having it proces
Two weeks, one rule, and hundreds of entries from nearly 50 countries. Here's who took the four titles, and a look at everyone who made the final ten! Entries opened August 20 and closed September 1.
I've tried both and been having this debate with myself for the last few days, on two Asus Ascent GX10s (effectively the same as 2x DGX Spark): DeepSeek-V4-Flash-0731 (official weights) GLM-5.3-Flash
https://preview.redd.it/c8za3q7shanh1.png?width=900&format=png&auto=webp&s=5716c30be6913c351403fb16cc60a59e3b116200 https://huggingface.co/spaces/multimodalart/h3-acceleration-arena From author u/apol
For a few days I've been working on creating a custom local-only harness for some work related research using Codex / GPT 5.6 Sol and the model feels not only dumber than usual, but straight up counte
I've been using Deepseek V4 Flash 0731 for a few weeks now and while I havent thrown it anything very hard, im quite happy with it. Using through antirez's great ds4 project. They've added support for
TimesFM-3 is the third generation of Google Research's zero-shot forecasting model, and the main change from 2.5 is that it handles multivariate inputs natively instead of being limited to a single se
I like Nvidia, Ollama, and Hugging Face. I wish each of them well. But one owning the other creates a huge conflict of interest. Separation of Powers is key for long term sustainability of open weight
If you are like me and use Google flow for NB2/NBP there is hope to finally ditch it. Let me start off by saying I’m not very impressed with any of the current local image edit or Image Reference mode
I have been trying to squeeze TTS stack down far enough to run in a $3 chip which has 512kb of SRAM without NPU. While trying to get to that milestone i built sanoTTS which has - 11 voices, 6 language
more sizes (probably still uploading): https://huggingface.co/IFM/K2-Horizon-32B-GGUF https://huggingface.co/IFM/K2-Horizon-7B-GGUF https://huggingface.co/IFM/K2-Horizon-3.7B-GGUF https://huggingface.
I want to share a short paper just published exploring a simple but surprisingly effective optimization for sparse MoE reasoning models. The idea: Instead of retraining anything, we just tweak the rou
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
Krea 2 Turbo — 4-Step Distillation LoRA (work in progress) A LoRA for Krea 2 Turbo that reduces the minimum usable step count from 8 to 4. ⚡ Half the steps — 8 → 4, on Turbo's own deployment sigmas. ⏱
I might be missing something obvious, but when I open any cloud model, by default I'm dealing with the 200k version and the first thing I need to do is /model to change it to the 1M version. Is there
75B MoE is an interesting size to check, you can run it today (no MTP support yet) The model employs a hybrid MoE architecture with interleaved Mamba, MoE, and Attention layers. Like Nemotron-3-Super,
I liked the Nvidia that focused on just GPUs for gaming, not on the Nvidia of today which seem want power consolidation. Modelscope is another platform for those that simply want to know an alternativ
Discovered that using a realtime llm + h3 world simulation, you can allow multiple different simultaneous character instructions that are restricted to only controlling their respective character. If
This is mostly for setting up for expectation, since personally without LLM i could take 3 days (15 hours of active programming) to debug or implement a feature, but with Qwen 27B (even before Qwen 3.
I’ve been running qwen on my personal Mac but I’m getting to the point where’d I’d like to have something always on, running various jobs, and some more ability to experiment and earn about fine tunin
Bruh wtf, i switched to new method now im pissed, 20 dollars a month for 60 api cost a month ?? Open code provides this for 10 dollars a month , and if I use a beast like kimi k3 I can drown my months
Everyone is talking about how the new credit system gives significantly less usage than the old Ollama Pro limits, but there's another part of the pricing that seems a bit crazy to me. For example, I
I’ve been experimenting with neural rendering outside a game engine and built a native Windows video player around it. It prepares a neural-rendered version of a video, caches it, and lets you switch
I have a few AI subscriptions and I kept running into the same problem. I had powerful models available, but I struggled to use them in real life in an organized way. I looked at projects like OpenCla
Quality: 81.1 → 87.7 (+8%) Speed: 35 → 29 tok/s (−16%) Runtime: 8m51s → 44m39s (5x longer) Output tokens: 18K → 78K (🤯) Noticeably better quality, but you're paying for it with tokens and time. Full b
Looking into the new Qwen architecture, I was curious if you could modify the Ngram PLE Table to make it work like a long-term knowledge database. It turns out that, with some limitations, you can. I
A potato can create a very cool RPG in 24 minutes Laptop I3 8gb RAM 0gb VRAM, Windows 11 llama-server.exe --host 0.0.0.0 --port 8080 -m modelsqwen3.6-35b-InstructQwen_Qwen3.6-35B-A3B-IQ2_XXS.gguf -c 8
ik_llama.cpp merged qwen4exp MTP support yesterday (PR #2369, mine, reviewed and tested by four other people on their own hardware). It's on main now, no fork or patch needed. Posting since the last c
Sharing some numbers because most posts on this model are either using a single 3090 or unified systems from what I've seen. My current setup: 2x RTX 3090 (PCIe 3.0), dual Xeon E5-2696 v4, 188 GB usab
https://preview.redd.it/5rs3d7vtgbnh1.png?width=950&format=png&auto=webp&s=df828c1146bc6167bd09d3300018aba426fc1ab2 Hey, r/LocalLLaMA ! We are releasing SupraGDN-5M. It's a tiny GatedDeltaNet (GDN1) b
Disclaimer: I'm the builder. --- Over the past 2 yrs, I was working on a SaaS ML project & got very interested in ML/DL/AI. As everybody else, there were some normal paths I took to build a solid unde
Upgraded my work LLM server from DSV4-Flash-0731 to the new Vision-Exp model and did this little test for fun to see quality comparison. Test was performed in Openweb UI connected to my vLLM container
https://huggingface.co/orangesouth/MinimaxH3CinematicRealism/tree/main https://huggingface.co/vpakarinen/better-human-motion-h3-lora/tree/main prompt integrated_multimodal_description: dy, [Shot 1] Ph
Hey everyone, Wanted to share a short paper we just published exploring a simple but surprisingly effective optimization for sparse MoE reasoning models. The problem: MoE reasoning models (especially
We have been working on an open-source, model-neutral agent harness for general purpose agents called TrueForge, and wanted to understand how much the harness itself actually matters. So we ran 14 tas
Continual learning isn't some fake term but a real goal and arguably one of the most important open ones in 2026. It roughly means: ‘building systems that keep acquiring useful knowledge or skills aft
The job of doing inference is not the same for every model ou there, as it depends on the architecture and configurations and the available machines. Many times we would not have the right machines to