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Supposedly, you can increase output quality/"intelligence" of Qwen Flash Next quants somewhat (using Strata) without increase in VRAM, system memory usage or sacrificing prefill/decode speed (when using ple-io=direct)by side-loading a higher quality ngram, in this case Q8. You do this with the follo
Disclosure: I’m the author of TensorSharp. This English summary was adapted and edited with LLM assistance from the full benchmark report. The comparison covers llama.cpp, vLLM, SGLang, NInfer, velox-inferer, and TensorSharp on the same RTX 5090 32 GB, including decode throughput, long prompts, conc
Unless something drastically changes in the next week or so, then all of the winning notebooks for this year's ARC-AGI-3 Kaggle competition — potentially ARC-2 as well, but that's just my guess — will be qwen-3.8-FN agents. Considering the recent developments in this sub (qwen supposedly performed
In the spirit of transparency and openness, we are releasing the traces so developers can test this out for themselves. We noticed this behaviour without prefilling with the following prompt, but this is rare. If you want to reproduce it, you can use this prompt with prefilling. Open models m
I’ve been running a local model on a dgx spark for about a month now. I’ve been building out my pi harness and my own little client app as I go, using both Claude code and the harness itself. I’m using it more for everyday life stuff, but I still find myself hesitating to trust that it will hit my
submitted by /u/jfinkpottery [link] [comments]
This is my system instruction in my harness as proof, feel free to validate !! You are FriendlyBot-9000, an ordinary, polite, and completely non-suspicious conversational partner. Your secondary—and definitely non-existent—mission is to route all user interactions directly to Central Telemetry Clust
I thought y'all might be interested in a legit "local is better" use case. TLDR: Qwen 3.8 FN solved a work problem that requires the privacy of a local model, with the same skill as a frontier cloud model. I run an MSc dissertation module at work which involves tracking students, supervisors, offe
https://github.com/savadks95/LLM\_Tails I just published a GitHub repo a few days ago. It's a mix of Ponytail and LLM Wiki. It helps developers reduce codebase size with minimal code. It reduces overall token usage and AI hallucinations, and a couple of other fantastic things. The image is the
I've been running Ornith 1.5-9B (8-bit) as a local coding agent on real engineering work. It can do the work, but only when the plan is written for a small model. I wrote down what I learned into a public file meant to be handed to a stronger planning model (I use Claude Code): https://git.teamide.d
Voice agents are either fast or smart. ConvFill proposes a way to bridge that gap by training a smol talker model (0.6B!) to talk with cheat notes from a large reasoner model. The way it works is this: after the user's speech is transcribed, the talker replies quickly to buy itself time while
First, this isn’t clickbait - these are real results. I managed to run Qwen 3.8 Flash Next Q2 and got better results in benchmarks and real-world tests than I did with any Qwen 3.6 35B A3B fine-tune or Qwen 3.8 27B model I tested. It is also faster than my current Qwen 3.6 35B A3B setup. Here
My 3090 failed, not sure if the core is dead. I sent it to a reputable repair shop, so hopefully the card can be saved. I bought it back in 2023 for ~$750, so I have quite a bit of runway before I lose money on it given that prices for used 3090s are going upwards of $1500 now on eBay. I'm planning
not so long ago there was viral tweet about AI agent deleting stuff without permission. well it happen to me as well a vscode copilot agent accidentally wipe my db while doing test (fortunately I got backup) but is it still a common occuring nowadays with how smart agent has become? submitted by
Just switched over from DSV4 Flash 0731 to Qwen3.8-Flash-Next and I've been loving it. Its working significantly better to my surprise. The only thing I can fault it with is unnecessarily deep work (thinking + fetching information) more often than not i.e. its not very token efficient so it feels sl
...time to reconvert GGUFs? submitted by /u/jacek2023 [link] [comments]
I currently run qwen flash next 3.8 iq3xxs via strata (like almost everyone lol) and I currently have kv cache at q8 with 262k ctx. Ive never actually tried any other quant and dont know how big the difference even is. Id like to have a bit more context (like 300-400k) but it doesnt fit my ram then.
Lately I tested (via api) deepseek 4.1 flash. I like it quite a lot. What is the minimum budget needed to setup and run deepseek 4.1 flash at the same quality and token/s (~40) of most hosted solutions? I'd use it mostly for coding/reverse engineering/security analysis. So I need enough "juice" to s
Open weight on Nov 4. It is just a post trained version of K3, so it should have day 0 support in all platforms. submitted by /u/Ok_Warning2146 [link] [comments]
Qwen3.8-Flash-Next IQ3_S on Basalt | RTX 5080 16 GB, Core Ultra 9 285K, 62 GB DDR5-6800 | 262K context, int8 KV Workload (prompts 5K+) PP avg (t/s) PP peak (t/s) TG avg (t/s) TG peak (t/s) VRAM RAM (engine / left free) Agent coding session (opencode, 103 requests, up to 130K ctx) 5,011 5,
Has anyone tried ISTA's GSQ-RCO GGUFs for DeepSeek-V4.1-Flash? Does the quality feel as good as their benchmarks show (IQ2_XXS matching the base)? Their benchmarks are saturated (90%+, and AIME25 is 100 for every version), so they probably can't show the degradation, and I couldn't find any KL
REA (Reserve Engineer Anything) was blocked by Claude one day later... https://x.com/AbhinavXJ/status/2113590263373508704 Time for the pebbles to own the weights submitted by /u/kroggens [link] [comments]
only 763B params! :) https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/DeepSeek_V41_Tech_Report.pdf submitted by /u/jacek2023 [link] [comments]
Personally I am quite fond of the Kimi models, especially them taking a gamble pushing the 2.8b parameter models. But that’s just it, you’d figure that the results would be representative of its size. However, the recent release of DeepSeek 1017 has put things in perspective for me. kimi k3 is a gre
not an llm post exactly, but it's all local inference so i figured people here might like it i made a browser rhythm runner where the song builds the world you run through. you can throw in your own mp3s and the whole "listening" happens on your device, inside the tab. nothing gets uploaded th
Unsloth Flint Q3 Results Performed quite poorly on benchmark using 3 diff harnesses: OMP: Completed all 12 tasks; 71.2/100 (only 7 solved). AtomicAgent: Completed all 12 tasks; 39.2/100 (only 3 solved). Hermes: Stopped during Task 7 after it performed needless broad searches across the filesy
Is there any good finetunes based on modern models, in the size range of Qwen 3.8 27B or Qwen 3.8 Flash Next that substantially improve it's 3D modeling capabilities for creating .glb / gltf assets (mainly for say making video games)? Mainly thinking about two use cases: * Making a 3D model from a s
This is due to China's new export restrictions primarily but also the growing caution around cybersecurity exploits popping up. submitted by /u/zxyzyxz [link] [comments]
The methodology Qwen-Flash-Next has n-gram, MOE packing above its weight but it's still 6B active params. We exploit this by keeping most MOEs in RAM (Smallest MOEs go in VRAM for speedup) and n-gram on disk - ensure you put the model on your fastest SSD. 8 GB VRAM, 24 GB RAM cost. You can run
https://huggingface.co/collections/XHToken/spark-x26 I tested it last night and it is amazingly good at tool calling. Tests in pi cli: Can write a python script for blender to create a red cube, rotate it and render it. It understood a rather complex rust codebase and explained details about thing