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I'm about to buy a Mac Studio mainly for running LLMs locally and I'm stuck between two configs: M5 Ultra (30/64) with 96GB: 1.2 TB/s bandwidth, roughly 1.7x faster generation and much faster prefill M5 Max (40-core GPU) with 128GB: 614 GB/s, but 32GB more memory and a bit cheaper The models I car
Many a praise have been sung on Qwen-3.8, but here is mine. Qwen-3.8 and I had a rocky start, because it thinks so much. Watching it working is painful, so you have to stop doing that. You have to let it work unsupervised. And that's okay, because it really is able to complete complex refactors on i
I posted previously my swe-verified django 100 tasks benchmark comparing different local models and quantization. No new models for now, but a fix in my evaluation workflow that was unfortunately not stable during the weeks/months of me using it. I redid the evaluation on all runs and here ar
UPDATE: https://huggingface.co/bartowski/Ling-3.0-flash-VL-GGUF Model Overview Ling-3.0-flash-VL inherits the language, reasoning, and long-context capabilities of Ling-3.0-flash, while extending them with native image and video understanding. The model has 124B total parameters, with only 5.5
Ok, so the Mac M5 Ultra (256GB) hit the market, but the only publicly available benchmarks material are flashy YouTube "clown influencers" videos. We need serious numbers to evaluate whether Apple’s silicon can actually compete with Nvidia’s current GPU‑centric workflows or not. Every damn video I w
Original post: https://www.reddit.com/r/LocalLLaMA/comments/1woscea/contrastive_language_models/ (sorry I felt it wasn't giving CLM the highlight it deserves) What it is: a new projection head for Qwen3-8B. github: https://github.com/Contrastive-LM/CLM hf: https://huggingface.co/Contrastive-LM At th
These boards cost me $115 each and I have them connected using llama.cpp with Vulkan and RPC on Bazzite. The boards have roughly 27GB of combined GPU memory and communicate over 1gb Ethernet. For around $300 including psu I’m loving the performance. I have a few more and want to see what 6 loo
Source: https://rogueon.ai/blog/rogue-deep-think-preview/tecnica Could this kinda stuff be the way for open models to prevail? Since open models are so much cheaper than, say, Astra and Fable, maybe just cranking up reasoning to the max can get us a bit closer. submitted by /u/krzonkalla
Thought it's about time to share after testing for a week. You need four things most people miss: the right quant, the right model, the right branch, and the right cache flags. https://github.com/dtm-beep/qwen38-flash-next-mtp-16gb TLDR: AtomicChat AD-4.27bpw Q4_K_M target + the shared Unsloth MTP
Got QFN up and running on our Strix box this past weekend and have been running on it for a few days now. Big thank you/shout out to the Halogen team, it's running fantastic on the Strix, this is clearly "the setup" right now for this hardware with this model, really impressive performance for such
https://huggingface.co/bartowski/LensVLM-9B-GGUF LensVLM-9B LensVLM is a 9B Vision Language Model (VLM) that scans compressed images of text, then selectively expands only the relevant pages to their uncompressed form via learned tools. Paper: LensVLM: Selective Context Expansion for Compress
If like me you enjoyed classics like Baldur's Gate 2, this is a small fun experiment. For anyone interested: https://www.youtube.com/live/8FhPfRKTucw?si=PMIVdHbZ-31PNwZD Qwen 3.8 Flash Next has amazing agentic capabilities but what about playing video games? There is some machinery I will open sourc
I’ve been playing with Nemotron 3 Diarization, and it fills a gap I’ve had with local voice agents: keeping track of who is speaking. It’s a diarization model, so it gives you speaker labels rather than transcriptions or people’s names. It can stream its output and track up to eight speakers.
I am proud to release these quants of Qwen 3.8 27B. They beat the excellent ISTA and Unsloth quants byte-for-byte on three corpora. Both KLD and top 1% were tested 3x. It took a week of continuous GPU and CPU time to generate these, all done on a single Strix Halo. https://huggingface.co/agentionai/
I hear Qwen code unlocks the model better. I also think it has more power user features than open code? It’s nice open code can work with multiple models easier though Thoughts? submitted by /u/FactoryReboot [link] [comments]
What it is. stuntd is a local proxy for typed LLM decisions: choice, yes/no, score. Your app keeps calling its provider through it. stuntd records the decisions, trains a small head on top of Laya (the open-weights System One model; the encoder stays frozen), serves it in shadow mode, and swit
• Ming-Image-0.1-Design, 6B • Ming-Image-0.1-Design-Layer, 6B • Two open-source Agent Skills: the Ling UI Design Skill and the Image-to-Editable-PPT Skill Ming-Image-0.1-Design ranks #1 among open-weight models on Artificial Analysis’s UI/UX Design leaderboard. https://huggingface.co/inclusion
Don't know how they did it, but for under 10GB model, the results are astonishing. I am running it on Unsloth Studio. They just released the update, so if you are not seeing the option, I recommend updating your Unsloth Studio. Cheers! submitted by /u/108er [link] [comments]
It is 12 points higher than the best open model mimo 2.6 pro and a big jump from fable 5.1. Crazy, glm 5.5 and qwen 4 will be on par with gpt 6 sol or better since it has a score of 48 If it took 2 months for the best open model to go from 44 to 46, then at this rate, in 6 months , they will reach 5
Someone recommended that I try the ByteShape Qwen 3.8 27B IQ3-XXS GGUF after seeing my previous testing of the GSQ quant. So I did. And the result was… surprisingly bad. For context, I'm running: RTX 3060 12GB 16GB DDR4 RAM, single channel CachyOS / Arch Linux llama.cpp Qwen 3.8 27B MTP/specu
Dynamic Quantiser TLDR - Makes dynamic and/or custom quants of ggufs. Doesn't need data, just gguf file + llama.cpp. Minimises cosine deviation - highly correlated with minimising KLD. Fast, much better than standard quants, not as good as Unsloth on pure text, maybe as good on mixed inputs/co
I've been running FreeToken on my 2x3090 box for a while and ended up maintaining a fork of it. Posting it in case it's useful to anyone else here. Quick context if you haven't used it: FreeToken is an edge-native MoE serving engine. It offloads experts to host RAM/NVMe and co-executes on CPU+GPU so
I am trying out a few fine-tunes of Qwen 3.5 9B @ IQ4_XS @ 131K context and trying to go mostly local (free beats cheap, after all). However, it is much slower than at, say 16K context, even when I am not actually using 131K tokens in the first place. Anyone know why this is? I am using the followin
This post is written by a human and I'd appreciate it if you treated it as such. Thanks. So, I've been noticing a pretty clear interest in developing as good a coding and agentic tool-calling model as possible, especially at smaller sizes, sub-50 gigs. However, I'm finding that at least for my use o
Been playing with judge models in my eval pipeline, so this landed at the right time. Kev is a small family of Jev-architecture decision models (0.8B, 4B, 9B) built on Qwen3.5, Apache-2.0, with training code and frozen eval suites included. The 9B fits a 32GB Mac in bf16, and it's API-compatible wit
Recently, the new DeepSeek-V4.1-Flash architecture showed how a causal encoder-decoder can work, but it was trained from scratch. Model Grafting does it to an existing model: cut at some depth, let the lower layers read the prompt, and use the upper layers get for encoder's residual stream as prefix
https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B That's serious. submitted by /u/Beamsters [link] [comments]
This experiment is live, you can inspect all the internal reasoning, memories, attempts here: https://artificium-covering-experiment.gr.bio/ The problem that the agent is trying to solve is a covering design problem: https://en.wikipedia.org/wiki/Covering_design Known as C(25,15,5), the best current
Since there’s no comparison chart on the model page, I asked Perplexity to compare it against some relatively small open-weight models in a similar size range. Here are the results. Upd. Terminal-Bench 4.0 results: MiMo‑V2.6‑Flash‑RL — 28.8% DeepSeek‑V4‑Flash‑0731 — 12.0% Qwen3.8‑Flash‑Next —
we're so back?!? submitted by /u/VoiceApprehensive893 [link] [comments]