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The earlier post on this subreddit by 20+ companies signing the petition including Microsoft, Meta, Nvidia, YC (https://www.microsoft.com/en-us/corporate-responsibility/topics/open-weight/) etc plus this https://xcancel.com/elonmusk/status/2080672505660834163 And the entire LLM enthusiast mark
mii-llm, an open source AI lab, released Zagreus-0.4B-por, a compact bilingual Portuguese–English language model pretrained entirely from scratch. The model has approximately 400 million parameters and is part of the Zagreus family, an ongoing experiment in building small open models focused o
The Open Letter was initiated by Microsoft and published today: “Open Weights and American AI Leadership”. It argues against broad or premature restrictions on open-weight models and explicitly says policymakers should distinguish legitimate model distillation from misappropriation. Notably a
I'm downloading it again now. So far, the model hasn't performed well with reasoning tasks, but I really appreciate the work being done to fix this. submitted by /u/LegacyRemaster [link] [comments]
https://preview.redd.it/b7ybs7nqx5fh1.png?width=3440&format=png&auto=webp&s=e6aaaa15cbe59debaae1ebb7fcd708167e86dc35 Hey r/LocalLLaMA ! We are back and we have something really amazing today. Our big 5M samples Reasoning Corpus dataset. This dataset features 5 million rows of: - repo_id --> wh
https://huggingface.co/swiss-ai/Apertus-v1.5-70B https://huggingface.co/swiss-ai/Apertus-v1.5-8B Apertus 1.5 is a family of 8B and 70B parameter language models designed to advance the state of multilingual, multimodal, fully open, and transparent AI. The models support a wide range of languages, ha
Shoutout to this awesome guy - https://www.reddit.com/r/LLM/s/IDUyU3v9ap Thanks to his project, BigMoeOnEdge https://github.com/Helldez/BigMoeOnEdge, I managed to successfully run a 35B MoE model on just 12GB of RAM! My setup is a modified Xiaomi 12 Pro (12GB RAM) that I call "Zeus". https:/
TLDR: I (with the help of AI) re-implemented every Blackwell-only kernel (DeepGEMM, FlashInfer sparse-MLA, block-scaled FP8) in Triton, because they simply don't exist for sm89. The performance is 2-3x more for parallel agentic workflows. Benchmark llama-server vs vLLM I was inspired by the po
Now live on OpenRouter, and free to use through August 3, 2026. Hoping they will going openweight soon~ submitted by /u/niacolhealth [link] [comments]
I could be reading it wrong but it looks like they REAPed their own 250B model down to a 32B model. They claim their 250B model beats max-think deepseek-v4-flash submitted by /u/giveen [link] [comments]
LLaDA2.2-flash is an agent-oriented diffusion language model in the LLaDA2 series. By introducing Levenshtein Editing (with DELETE and INSERT control tokens) to diffusion language modeling, it represents the LLaDA2 series' first step in agentic applications, including long-context tool use, mu
I got 192 GB of Vram to use and a long horizon agentic task about coding, I would like opinion from ppl who have had extensive experience on all 3 of those models . Whats the best quality (should include some cyber and devops knowledge) ? Whats the best quality/cost ratio? Thanks!!!! PS:Any other mo
from kwaipilot: Following the release of KAT-Coder-V2.5 in July, we are pleased to release the open-weight version KAT-Coder-V2.5-Dev, an MOE model with a total parameter count of 35B and 3B activated parameters, to strengthen communication with the community and showcase our research achievem
A Chinese article compiled 52 remarks from Liang Wenfeng’s four-hour investor meeting. I’ve summarised the most important ones below. DeepSeek has one central objective: AGI. This is not the time to maximize returns through products. Products are one rung on the path to AGI, but we do not need to d
AI9Stars has released G9v3-3B an open weights language model designed to deliver strong reasoning capabilities within a lightweight 3 billion parameter size. It is released under the Apache 2.0 license making it fully open for personal and commercial use The best use case is a general ai assis
Every time a strong open model drops, the same cycle plays out: ai bro's claims it's "just distilled from GPT4/Claude/whatever," case closed, move on. I think this take doesn't hold up as well as people assume. A few points worth separating out: Training on outputs isn't the same as real distillatio
There's a bunch of small MoE with around 1B active params, like LFM2.5 8B A1B and Granite 4.0h 7B A1B; and then there are models with 3B+ like Qwen 3.x ~30B A3B and Gemma 4 26B A4B, but those are already on the heavier side if you don't have enough resources. What about the middle ground, MoE with a
I've got some time tonight and am excited to run it through my agent loops and form my own opinion. Wanted to hear everyone else's as well, but without the barchart noise. submitted by /u/ForsookComparison [link] [comments]
interesting to see the reverse of the American frontier model makers' stance coming from the Chinese side via South China Morning Post submitted by /u/zxyzyxz [link] [comments]
If many people have noticed that there's no reasoning phase in Laguna S 2.1. I noticed the Poolside development team updated the chat template twice in the last 24 hours. There was a bug where, if preserve_thinking was disabled, reasoning wouldn't start at all. However, I don't think that's the roo
Fara1.5-27B is a multimodal computer use agent (CUA) for web browsers, from Microsoft Research AI Frontiers. It observes the browser through screenshots and acts on the user's behalf by emitting structured tool calls — click, type, scroll, visit URL, web search, and so on — to complete tasks e
Hey HN, Henry & Roman here from Cactus. A small, on-device model is fast and private, but sometimes wrong, but frontier models are getting expensive pretty fast. So, we post-trained Gemma 4 E2B post-trained to know when it's wrong. Every response comes with a confidence score between 0 and 1.
The primary driver of this project is that I'd become frustrated with the reasoning behavior of smaller local models such as Qwen3.6-27B (i believe particularly at lower temperatures, and where system prompts are highly specific), their reasoning process is highly unreliable and often tends to
We’re open sourcing an alpha release of NeuTTS-2E: an on-device TTS model with 125M active parameters and 7 controllable emotions. The goal was simple: when you select “angry,” “fearful,” or “happy,” the delivery should follow that instruction rather than whatever emotion the model infers from
I built Encode Bench, an open benchmark that asks a model to solve a task and return the answer as a Base64 payload. The initial result surprised me: across the eight models with matching data in the current nine-model snapshot, Encode Bench pass rate has a Pearson correlation of 0.91 with the
From Peter Gostev on 𝕏: https://x.com/petergostev/status/2079825961718046974 submitted by /u/Nunki08 [link] [comments]
Solar Open 2 is Upstage’s 250B-A15B open-weight large language model, built for agentic use cases such as office productivity, document-intensive work, and coding. Its Hybrid-Attention Mixture-of-Experts (MoE) architecture with linear attention delivers highly efficient inference even in long-
Benchmark Solar Open 2 250B-A15B Solar Open 100B 102B-A12B Command A+ 218B-A25B Mistral Medium 3.5 128B dense, high MiMo-V2.5 310B-A15B DeepSeek-V4-Flash 284B-A13B, max Know. & Reasoning MMLU-Pro 86.2 80.4 79.0 81.2 84.6 85.9 GPQA-Diamond 86.3 66.2 75.6 77.5 83.0 88.9 HLE (w/o
https://huggingface.co/poolside/Laguna-S-2.1-GGUF https://huggingface.co/unsloth/Laguna-S-2.1-GGUF https://huggingface.co/poolside/Laguna-S-2.1 Laguna S 2.1 is a 118B total parameter Mixture-of-Experts model with 8B activated parameters per token, designed for agentic coding and long-horizon w
this strategy effectively uses vram as cache over disk to keep MoE experts on cuda compute path in llama.cpp. numbers first. detailed explanation down below. Numbers on dgx spark Kimi-K2.7-Code.i1-IQ_S.gguf 204GB 1T.A32B https://huggingface.co/mradermacher/Kimi-K2.7-Code-i1-GGUF run method pp512 t