Search: “context”

First off, I know that GLM, Qwen, and DeepSeek absolutely dominate in terms of SOTA Open Source models, and that’s what I use in my personal projects and for school, however, I’m also responsible for

Hi! I've been following this community for quite a while and have difficulty figuring out what to put on my 3080 12gb - I know Qwen 3.6 35B 3A was the go-to choice when it first came out, but I'm curi

i've been thinking about this more after trying different agent setups. the model isn't the only thing that determines how well an agent performs. The harness around the model matters a lot too. With

The downside of uncensoring a model is that it is known to potentially damage it, but CyberTiel is an even more capable software engineer than its censored TielCoder base, while allowing offensive sec

Running the full deepseek-ai/DeepSeek-V4-Flash-Vision-Exp on consumer Ampere — 10-12x RTX 3090, SM86-compatible vLLM build. 285B MoE, FP4 experts + FP8 attention, 157 GB weights. Highlights: - **60+ t

What is Astra actually supposed to be better at, Because if the answer is “everything,” then I genuinely do not understand what kind of testing was done for writing-heavy use cases. My biggest issue i

I've been making a lot of comments about optimal setup for Strix Halo (gfx1150) and from my observation, 90% of our community is using offcial llama.cpp for it, which is NOT optimized for Strix Halo a

Before the introduction, two caveats. This isn't anything as spectacular as the mmh3 stuff filling these subreddits these days, so please go easy on me. It's an experimental tool I'm sharing in its ra

Our research team has been exploring an alternative approach to achieving interactivity and better responsiveness with LLM systems. One of the team members wrote up a post about it: https://research.y

I've built and open-sourced ParaRNN, a PyTorch/Triton library that parallelizes the training of non-linear recurrent architectures across time using Newton-Raphson iterations mapped to associative pre

Time for another update! We have been busy and managed to improve the gains substantially (mostly from exploring existing llama cpp PRs and adopting relevant things). Among other things the README.md

OK, you guys want to start with local vibecoding but don't know how. In the discussions, you recommended that I use Godot. I downloaded it without knowing what it was. I was expecting a library, but i

I was running into a lot of posts that praised both the Fixed template and the Sharp template in comparison to the stock one, so I put them to the test. It's not as extensive as it should be for a pap

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'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

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.

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://preview.redd.it/6e9xb57a42nh1.png?width=787&format=png&auto=webp&s=ffae7996bbf8ab00498cc62c733e7597dc550f24 I'm not sure how many people care about Android Studio, but I think it's cool that G

This came from another thread or comment. I forgot exactly where, but the basic idea was to replace the 51B N-gram layer in Qwen 3.8 Next with a much higher precision version. Someone running a 5090 r

https://preview.redd.it/6h4xc5o8l6nh1.png?width=1158&format=png&auto=webp&s=b65074b6baaa1faa2347e5259229c8ba803bcd4b Here is link to repo: https://github.com/perplexityai/pplx-garden/tree/main/lily It

I only started using some vLLM forks recently in a 4 x 48GB 4090 system. DS4F - ~5000pp/180tg (DSpark) Qwen3.8 Flash next - ~7500pp/135tg (MTP) This is amazing, like having the API in my house. But it

Hi all, I have recently been experimenting with different LLM set ups and after everyone was raving about how good Qwen 3.8 27b was, I was inspired to try and get it deploying. After some battling wit

## Qwen3.8-Flash-Next-NVFP4 (inferact) vs Qwen3.8-27B-FP8 (qwen) Slammed with work and no time to pretty this up. Qwen wrote most of this but I checked the data. All tests done on the same rig, same p

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

So, ideally for this thread we exclude the ones that everyone on here is already well aware of and discussing on here a lot, like N-gram, quantization improvements, MTP, D-flash, and D-spark, since th

When using two GPU with llama.cpp with --split-mode layer you usually provide a --tensor-split value to fit the proportion of the allocation on each GPU, say you have 16GB + 12GB something like --tens

I was bored and decided to test some of the image generation. You can see from the images that I got *very* consistent patterns. These were only a few, but I was approaching 20 generations before I st

After a couple of days of faffing around, here's my current config in case it helps anyone out. Some of this is also valuable for a single Spark. AI slop below for you to read and paste into your own

Hardware: rtx 5080 16 gb vram; 64 gb ram ddr5 6000hz; ssd with unlimited memory; ryzen 7 9800 x3d. OS: Windows 11 Software: I’d prefer llama.cpp, but it’s not a strict requirement; I’ll use whatever y

I've been running Qwen3-0.6B on the M5Stack LLM-8850 card (Axera AX8850 NPU, 24 TOPS, 8GB LPDDR4x) hosted by a Raspberry Pi 5 — as a llama.cpp backend. The problem: the vendor stack requires convertin

A few days ago I posted asking what people considered the best local model for an 8 GB VRAM GPU. At the time, my personal sweet spot was Qwen3.6-35B-A3B, for agentic coding with Pi.dev. Well… Thanks t

We're releasing Qwen3.8-27B quantized with our newest methods, GSQ + RCO. Higher-quality models, same file size, now with the search and the quantizer both learned. What's inside: GSQ (Gumbel-Softmax

Hey all, and hello fellow DGX Spark-ers! Today I managed some pretty crazy numbers: 181 tok/s aggregate on 2× DGX Spark on Qwen3.8-Flash-Next at 512Kcontext (2.8M kvc) I hit 181 tok/s aggregate today

Ever since Qwen 3.8 Flash Next dropped, there's a misconception going around that N-gram tables will let people run 1T+ parameter models on a single server with 980B parameters offloaded to SSD. I'm h

Got the new Qwen3.8-Flash-Next (176B total, 6B active MoE) running locally and figured I'd share for anyone else wondering if their setup can handle it. **My hardware:** 192GB DDR5 RAM, RTX 5070 Ti (1

Upgraded to a 5070 Ti so I could run Qwen 3.8 27B, which works perfectly, but didn't want to let the old 4070 Ti go to waste. The cards would touch if I put them both in the PC and I knew the heat wou

Megathread for discussing the release of GLM-5.3-Flash. Quants Fine-Tunes & Abliterations Chat Templates Inference Server Support & Configuration Experiences, Benchmarks & Model Comparisons We'll try

Benchmarked qwen3.8 xhigh, medium and muse glimmer. Xhigh effort mode with qwen3.8 took almost 30hrs. (And still failed on 16 cases because of the 32K output token limit) Medium effort mode and muse g

Try it here: https://ocean.blackbeardlabs.dev/ Model: Qwen 3.8 27b Q8_X_KL Unsloth Hardware: 3 x RTX3090 Harness: DeepSeek Harness Prompt: /goal I want you to create a **JavaScript + Node.js WebGL pro

This is one more vibe slopped custom optimization for, in this case, my hardware (m2 ultra 60 cores, 192gb). It is just a fork from llama.cpp with a few changes, it achieves: - DeepSeek V4 Flash, no k

I was fascinated by Nanbeige's outstanding performance for its size, so I started digging into how much a model can improve its own representation just by looping over itself (for fun). My prototype w

Row-Bot v4.8.0 is now available. This release adds provider-aware reasoning controls, letting each chat keep a valid reasoning choice for the exact model in use. Depending on model support, you can se

Off a single prompt, given my credentials and the name of my university, qwen3.8-27b was able to successfully pull my class schedule from the kinda shitty and convoluted web of university websites. It

Hey everyone, Posted TinySearch here a few versions ago and got a bunch of useful feedback, so figured I'd post an update because the thing has changed quite a bit since then. Repo: [https://github.co

llama.cpp pr #27342 adds dflash2, so i rented an rtx 6000 and ran the same four prompts through four decoding setups on qwen3.8 27B median results over the four tasks: baseline 47.4 tok/s mtp 114.7 to

Here's the DFlash2 announcement, and I was pretty excited for this after trying out DSpark on llama.cpp a few days ago and being somewhat disappointed that it wasn't really working. Anyways, I spent a