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I’ve always had this idea but can’t prove it. I think Anthropic and OpenAI don’t really have any secret sauce, their moat is just scale. Rumor has it Opus has 5T parameters and Mythos/Fable are 10T parameter models, while open models stayed under 1T for a long time. Only recently was that ceiling br
With all of the attention Kimi K3 is getting at the moment and likely for the coming weeks, would we be seeing Dario Amodei continue to further push to rid his competition with more fear mongering? submitted by /u/Hannibalj2ca [link] [comments]
Unbelievable to see kimi k3 beat frontier models that were 'too dangerous' for public use. submitted by /u/Gohab2001 [link] [comments]
The gap is no longer measured in months. Open-weight models are catching up in real time, and Kimi K3 is already performing near Fable level. At this point, open models are not simply following the frontier anymore. They are on the verge of overtaking it. Kimi K3 may be one of the clearest sig
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https://mp.weixin.qq.com/s/V4xhEIy8xDXSMDPrPkmUAQ Their verified account. English version just released: https://www.kimi.com/blog/kimi-k3 submitted by /u/Different_Fix_2217 [link] [comments]
Everyone's probably seen the remotion thing that went viral a couple months back with CC. Its basically that with Kimi K3 as the model provider. Prev. example with GLM 5.2: https://www.reddit.com/r/LocalLLaMA/comments/1u8kyqf/glm_52_release_video_made_with_glm_52/ Feels much better than 5.2 in
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Luciole-23B-Instruct-1.1 is a fine-tuned and aligned version of Luciole-23B-Base, an open-source, multilingual causal language model created by OpenLLM-France. Luciole-23B-Instruct-1.1 was developed by LINAGORA and the OpenLLM-Franceconsortium as a part of the OpenLLM France project, funded by
I was hopping that Kimi would be 2t, but nope is huge @ 2.8t!! (tears falling) That will make it more difficult to run decently. I hope the Unsloth team can make magic with this model submitted by /u/Hannibalj2ca [link] [comments]
https://preview.redd.it/4uqr0aggildh1.png?width=824&format=png&auto=webp&s=cdc3ece2cd45914092d83bd3dd233b17d95d3f54 https://preview.redd.it/ertqvxhiildh1.png?width=998&format=png&auto=webp&s=5ed93d8dc450fad8c88cd7fcd0b1c52c185c9f0b https://preview.redd.it/o0ml5kdvildh1.png?width=318&format=png
Link to the report itself is at the bottom of the page. submitted by /u/pegasus912 [link] [comments]
This is an an insane budget box that I've been using to test out a 98GB model using cpu generation on a 6 core CPU, 16gb vram.. for science. This week it went from 2t/s -> 7t/s on DeepSeek-V4-Flash-UD-Q2_K_XL, which has a 98GB vram requirement. Somewhere between b9986 and b10034 the llamacpp
Specifically Qwen3.6-35B-A3B-uncensored-heretic-Q8_0.gguf, temp 0.0, "Create an SVG of a Darth Vader." (In general text use, anything 4 experts or less seems to seriously break down. (8 is the default for this model). submitted by /u/temperature_5 [link] [comments]
I managed to get this model working on 2x 3090s with full 262k ctx and N=4, if anyone is interested to try it, thanks to this quant: https://huggingface.co/danielrmay/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-W4A16 Nemotron-Labs-3-Puzzle-75B-A9B (W4A16) on 2× RTX 3090 — vLLM, no CPU offload, full 262K,
Stack: i7 12700K | RTX 3090 TI | 96GB RAM Qwen 3.6 27B Q3/Q5 KXL UD I've been pushing Qwen 3.6 27B above 200K ctx all week, and it handles it like a champ. I'm impressed. Today I hit the ceiling at 262K and it's still functioning well and coherent. I'm planning on trying out Yarn to see if I can p
Suggesting Kimi k3 to be released soon. https://x.com/Kimi_Moonshot/status/2077521842080817296 Already on arena under codename kivine: https://x.com/AndrewCurran_/status/2077433196556554306 Post says good but slower than fable: https://x.com/Lentils80/status/2077387333154857151 Already a review on
Two DGX Spark and a Connect-X7 cable give you about 250GB of usable memory for $7000 8000 USD. This allows using some interesting models at 4-bit. For what seemed like an eternity, the only serious models in that size were GLM 4.5/4.6/4.7 (194GB), Qwen 3.5 397B (210GB), and older MiniMax. Then 3 mon
I've been using DS4 flash as a local "big brain" for when my qwen 3.6 35b a3b gets stuck on something, and for planning. I'm blown away by how capable it is at a 2 bit quant Has anyone used the GGUF at a similar 2bit quant and compared performance? I've got a Nimo Strix Halo box, 128GB, kyuz0 stack
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The full quote: I realize that some people really dislike AI, but this is an area where I'm willing to absolutely put my foot down as the top-level maintainer. Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it. Or j
MODEL + GGUF : https://huggingface.co/InternScience/models?search=a1-4b Technical Report Benchmark Qwen3.5-4B Agents-A1-4B Qwen3.5 Qwen3.6 Nex-N2-mini Agents-A1 🧠 Dense Models (~4B) 🔀 MoE Models (35B-A3B) 🔍 Long-horizon Search BrowseComp 47.2 66.8 61.0 67.9 74.1 🥇 75.5 XBench-
Don't get me wrong, all the big models are amazing, and every contribution to open source models is great. But I'm GPU poor and I can't use them locally. I'm currently running gemma-4-12b-it-qat-GGUF:UD-Q4_K_XL as my personal chat assistant, and I am so so happy with it! I still can't believe
Benchmark Results Benchmark configuration: threads = 96 type_k = bf16 type_v = bf16 Llama-3.1-8B-Instruct Q8_0 Prompt Size GGML_CPU_Q8_0 t/s ZenDNN_Q8_0 t/s Gain 256 472.28 730.87 54.75% 512 450.86 832.48 84.64% 768 446.81 864.52 93.49% 1024 439.58 800.15 82.03% 2048 405.07 778.
Efficiency defined as score over active parameters. Removed all the models that were not on the pareto frontier. Yes I'm aware that artificialanalysis.ai aggregate benchmark isn't perfect, but I have found it to be a good overall indicator. submitted by /u/StupidScaredSquirrel [link
Introduction RxBrain (Hy-Embodied-RxBrain-1.0) is a unified multimodal foundation model for embodied cognition — a single model that couples language reasoning with visual imagination to deliver three core capabilities: 🤖 Embodied Understanding & Reasoning — question answering and chain-of-th