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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 files compared to gemma / muse (which I know are better at creative writing). Im currently using qwe
So Kimi K2 is outdated, and so is GPT OSS 120b.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 blind alleys of my own bad ideas many times) and agentic coding (more sycophancy less bug noticing).
Given the commentary on the Q3.8FN release page here https://qwen.ai/blog?id=qwen3.8-flash-next I assume/hope that all the work that's going on to optimise the hell out of running it will be useful when Qwen4 drops? submitted by /u/demomanca [link] [comments]
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 workstation and no multi-GPU setup. I’m running it with TensorSharp, my open-source local LLM i
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 tok/s generation) and sharing it with the community now so others can also have the benefit. https://
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 base, not who actually calls it. And more essentially, Claude Code (my major coding agent) mainly uses
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 verificare personalmente come si comportano nello stesso contesto lavorativo. E il motivo per cui mi in
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 harness to control the browser client and play the game by sending signals over a websocket. Th
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 the transcript, summary and action items after. But I'd really like to move more of this local, esp
I've seen people reporting running it with 12GB VRAM + 64 GB RAM. Also with 64 GB RAM unified in Macs, but I was wondering if it's possible with any inference backend to run it for example on a 64 GB RAM minipc+ iGPU (780m in my case). I'm currently running 125B Ling 3.0 flash at Q2 quants with llam
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 bottlenecks. First, spinning up Chroma or another vector database alongside an 8B model just to chun
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 browser-based free version AI Dungeon 2. The setup is pretty straightforward: one person hosts the
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 using open source libraries like TRL and the Harbor framework for RL environments. We hope you fi
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 got rewarded with an absolute masterpiece of fraud: Promised: Intel N150 + DDR4/DDR5 Delivered
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 attention, designed to be fine-tuned into task-specific models (classification, token classification, re
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 11.5 tok/s, up from roughly 7 tok/s on the inherited setup. In normal conversations I’ve seen 1
Qwen Flash Next now uses less VRAM submitted by /u/jacek2023 [link] [comments]
submitted by /u/asankhs [link] [comments]
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 of real robot data • One model handles 64 tasks (10 whole-body + 54 tabletop) • Supports paral
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 language, reasoning, coding, agentic, and multimodal tasks. FrogNano inherits Qwen3.5-4B's dense 3
MoE speedup, but only for some MoE architectures (like Qwen 35B A3B) submitted by /u/jacek2023 [link] [comments]
I'm currently choosing between IQ3_XXS Qwen-3.8-Flash and Q8_0 27B. This month I don't have anything complex enough to justify either's potential so I've got a fairly bad read on how these two stack up in terms of intelligence. Have any of you compared the two enough to speak to which you've had a b
**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 file or tool result my agent reads on a 24 GB M4 Pro MacBook is a wait, and 64k of 8-bit cont
Basically a DGX Spark minus the ConnectX-7 ports. I’ve seen expected initial pricing from like $1800 to $2900. Not sure what configurations are actually at those price points. It’s all Internet hearsay until we actually see these things ship, but it’s nice to know that it’s potentially around the co
submitted by /u/Megneous [link] [comments]
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 flag. Pwn, web, crypto, rev, forensics, a few real CVEs and some multi-stage ranges. 19 tasks, 6 model
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 https://huggingface.co/ggml-org/Kev-4B-GGUF submitted by /u/jacek2023 [link] [commen
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 QA test (like giving a long medical history of 120k tokens and ask a question based on that medical h
This is 6 bc-250 ex mining boards with 5 in the asrock 4u12g case they came in. After a lot of testing my current preferred setup is 4 boards running Qwen Next Flash IQ2_XS at 100k context with around 28 tok/s for short generation and 24 tok/s at 50k with around 115 ppt. The other two boards r
submitted by /u/psychohistorian8 [link] [comments]