Qwen 3.8-27b coming this week
Confirmed by the official Qwen account. submitted by /u/Bestlife73 [link] [comments]
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I gave DeepSeek V4 Flash basic vision by training a 40M connector on 100K examples
I wanted to find out whether a huge text-only MoE could be given basic vision without retraining the language model itself. The short answer is yes. I froze DeepSeek V4 Flash and a 417M-parameter MoonViT image encoder, then trained a 40.1M-parameter connector between them on 100,000 image-text
1 Day in and I feel okay saying Muse-Glimmer-30B finally beats 3.6-27B for the size in some use-cases
A few things right off the bat: it reasons very efficiently. Like Grok 4.5 levels of efficient thinking it quantizes very well. My first few tests with iq3_xxs were better than Qwen/Gemma behaved at that size its knowledge depth is amazing. It beats Qwen3.6 27B on no-tools trivia. in OpenCode it
Observations on Muse-Glimmer reasoning traces being noticeably different from qwen / gemma models and questions for you guys
Just downloaded the model, UD-Q5_K_XL quant, asked it to generate a long story to test out reasoning and speed with dflash (super fast btw, ~ 90 to 160 tok/s on a 5090 depending on task) and was surprised at the reasoning traces, they are so unlike anything i've seen recently either in gemma 4, qwen
Muse glimmer benchmark
Little less smart than Qwen, but way fewer tokens per task. submitted by /u/NoFaithlessness951 [link] [comments]
I made a web-design benchmark for local models (Muse Glimmer 30B vs Qwen 3.6 27b vs Deepseek V4 Flash 0731)
submitted by /u/ShadyShroomz [link] [comments]
Please Share Your Experience About Muse Glimmer
I have a classic test for local LLM's. I asked for 8 ball pool game with only one HTML file and Muse Glimmer spend 21k Token(I m using full context so 128k) and only created a 220 lines of HTML and said its done. With my experience its not even close to Qwen 3.6 27B and we are waiting for Qwen 3.8 2
Best open-source harness like Claude Code?
Avid claude code user here looking to do equivalent things with local models. Just want to plug in something like Qwen and have the interface be 1:1 with claude code. Any suggestion? submitted by /u/Neighbor_ [link] [comments]
Needle 2: 14MB agentic LLM for phones, wearables, smart home and robots.
Hey LocalLlaMa, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggesti
inclusionAI/Ling-3.0-tiny · 8B A1.3B MoE· Hugging Face
Looks like the Ling team open weighted a much smaller version of the Ling-3.0-flash they open weighted a few days ago. It's 8B params with 1.3B active, and seems to fall between the 4B and 8-12B Qwen and Gemma models in terms of performance. Should have a massive tokens/sec on most systems. I
1M context with 17 GB model in 24 GB VRAM: "for the first time I was able to load a context of almost 1M tokens and extract 7 needles from various parts of the text"
https://preview.redd.it/xxjh11f38jih1.png?width=1852&format=png&auto=webp&s=76850ed51e29a8bc86c2ca718d4320075eed4363 Just wanted to share a user report that I found to be very interesting. Some person with an intriguing name manu69x managed to run 1M context on a single RTX 3090 with a model b
Comparing how Cline, Kilo, and Qwen Code handle long-task context/state (and why context loops keep happening)
I've been comparing Cline / Kilo / Qwen Code lately since they all handle long-task state differently. Cline: has Focus Chain, a markdown file kept outside the conversation that gets reinjected on a cadence, plus Memory Bank for project context, plus a standalone gRPC server so it's not fully tied t
Introducing Muse Glimmer: an open-weight model optimized for always-on local agent workflows
Hi r/LocalLLaMA 👋 Today we’re excited to release Muse Glimmer, a 30B open-weight model built specifically for local agent workflows. We’re releasing the weights to the community under a permissive Apache 2.0 license. A few specs 30B params, dense Multimodal: interleaved text + images via a d
Chat UIs with native audio input for multimodal models?
I've been running Gemma 4 E4B with oMLX and I can't find any chat interfaces that directly send the audio file to the model instead of running the audio through a separate STT layer. I can confirm the audio layers work because I ran a couple of requests through Pydantic AI in the Python REPL. Thanks
Why Speculative Decoding went mature in 2026?
Spec-dec has been a thing for a while, in fact, it's wasn't an idea that was born for LLM inference. E.g. Uber's https://github.com/uber/submitqueue applied it to a merge queue. Apple & GDM had been releasing papers on it since already 2022. Seeing it being mature enough for the big frameworks to ad
MiniMax H3: A New Open-Weight Video Model, Live in ComfyUI
MiniMax H3 is an open-weight, general-purpose multimodal video generation model that works across text, images, video, and audio. In ComfyUI, you can use H3 for text-to-video, image-to-video, first- and last-frame generation, and reference-driven creation. H3 jointly generates the visuals and
Running Qwen 3.5 35B A3B-Q8_0 gguf on a cheap radeon 7600 at 18 token/s
I also have 64 gb ddr4 ryzen 5600 Using llama.cpp Ubuntu distro Settings are as follows --n-gpu-layers 999 \ --n-cpu-moe 37 \ --no-mmap \ -ctk q8_0 \ -ctv q8_0 \ -fa 1 \ -c 9000 \ submitted by /u/Sweaty_Perception655 [link] [comments]
omlab/VLX-Seek-1.5-10B · Hugging Face
VLX-Seek-1.5-10B VLX-Seek-1.5-10B is the open-source 10B model in the VLX-Seek 1.5 family, designed for fine-grained perception and visual grounding in embodied scenarios. It targets practical settings such as drones, robots, robotic dogs, surveillance cameras, inspection systems, and other ed
Need real world ML problems to evaluate my educational ML tools
I'm a retired platform engineer, coding mainly in Rust, and involved with a ML study group. I developed a ML programming language (alternative to Python, Colab) to help me learn (and teach) ML concepts, visually. It has slowly grown to demonstrate a lot of concepts. It can do training on CPU (in br
endless-frontier/BigBang-v1 - qwen 3.5 finetunes
table bench https://huggingface.co/bartowski/endless-frontier_BigBang-v1-GGUF I'm downloading this model only because Bartowski converted it to .gguf, so it might be interesting. Doubts : The headline number is basically meaningless. "Performance between DeepSeek Flash (old one) and Pro" okay,
DeepSeek-V4-Flash-0731 Q8_K_XL sometimes stops mid-task in OpenCode - anyone else seeing this?
Hey everyone, I've been experimenting with the new DeepSeek-V4-Flash-0731 release locally using the Unsloth Studio Q8_K_XL GGUF with OpenCode. Overall, it's been working really well, but I've noticed a strange behavior during longer agentic coding sessions. Once the context gets above ~100K tokens,
DeepSeek v4 Flash 0731 locally on CPU
After seeing the benchmark results for the full release of DS v4 Flash 0731, I replaced my 2 x 16GB DDR4 ram sticks with 2 x 32GB DDR4 ram sticks to get a max supported of 128 GB RAM, in hope to be able to run GLM 5.2 equivalent model locally i.e. DS v4 Flash 0731 I also have RTX 4090 & Tesla P40 so
AMD llama.cpp: reducing MTP buffer overhead gave me 64K → 149K context for Qwen 27B
Available context length with and without the patch: Model: QWEN 27B ROCm stock patched Vulkan stock patched IQ4_XS Pure, single 16GB GPU 19.456 76.032 68,352 78,592 Q6_K_L on 16GB + 12GB 64,256 149,248 68,864 151,296 The issue is that llama.cpp overestimates the memory needed for MTP compu
Best Embedding + Reranking Model
What Local Embedding + Reranking Models are you guys running for RAG? I went down this rabbit hole because I wanted a Embedding Model + Reranker for a Translation Memory Server. Essentially, given X phrase/word/sentence/paragraph in English, can it find a similar phrase/word/sentence/paragraph in
Updated benchmark: Deepseek V4 Flash on SlopCodeBench (local)
Howdy - I posted a benchmark here - https://www.reddit.com/r/LocalLLaMA/comments/1vbtiy7/deepseek_v4_flash_on_slopcodebench/ This was using the hosted API - since then I've been playing around with quants Here is the lastest benchmark - https://github.com/michaelasper/benchmarks/blob/main/deepseek-v
Memory Bandwidth problems with Intel Sapphire Rapids
I have a Xeon w7-3465 and 4 sticks of RDIMM DDR5-4800 with a theoretical max bandwidth of 153GB/s. I am trying to run DeepSeek-V4-Flash-0731 as it is an MoE and the weights are in MXFP4, so I should roughly process 150GB/6.5GB tokens per second as it has 13B Experts. The problem is, I've only
ds4 flash 0731 UD-IQ2_M wrote a custom metal kernal for kimi k2 IQ1_0 in about 50 minutes
as a programming ignoramus this kind of thing seems extremely impressive to me... maybe others can shed light on whether this is expected from this level model at q2. DS4 IQ2_M just spent about 50 minutes writing a custom metal kernel after it could not find one on github for unsloth's IQ1_0 Kimi k3
No wonder Qwen and Gemma are so different
Pasted the same HTML/JS code (330 lines) into Qwen 35B A3B and Gemma 26B A4B. Qwen: tokenized the input to 1609 tokens Gemma: tokenized the input to 4258 tokens. Damn. I've never noticed this before and I haven't seen people mention it. That alone helps explain why Qwen is regarded as better at codi
Kimi K3 (Unsloth) IQ2-XXS from 711GB down to 478GB!!! Only Multi-language was removed to trim the size
Firstly a big thanks to the poster "hellohazine", he basically only removed the multi-lingual fat of the model and just kept the English language intact. It is the exact model, and the rest of the model still intact with all of its high intelligence. I think that was a brilliant move, and more of th
Extremely slow DSpark draft model performance (1-2 t/s) with DeepSeek-V4-Flash on llama-server compared to MTP?
Hey everyone, I could use some advice on setting up speculative decoding correctly with llama-server. My Hardware: GPUs: RTX 4090 + RTX 6000 Pro (120GB total VRAM) RAM: 32GB I am currently testing the DeepSeek-V4-Flash-0731 unsloth model using the 137GB Q4 UD-IQ4-NL quant. When running the MTP dra