2026-11-06

Big update to the Delta Inference Engine, you guys asked for more features, more benchmarks and less clickbait. I'm here to deliver The short version: I've added almost everything requested and more. Delta Inference Engine now supports 64/96/128 GB of RAM and fully supports the new Unsloth quants. Unsloth IQ2 runs at the same 41 tok/s (192 GB RAM) using a new native reader for the Engram tables and some clever CPU kernel work. Compared to the old default model on my 99-question benchmark it went from 52 to 57 correct, against 59 for the full model. It's no full deepseek model quant, but they're a lot more usable than you might expect. Speed on one RTX 3090 24 GB (i5-13600K, 4x48 GB DDR5-5600, PCIe 4.0 NVMe, Windows, no WSL). Writing speed, tok/s, greedy, one chat at a time: RAM UD-IQ2_XXS, 2-bit, 301 GB UD-IQ1_S, 1-bit, 233 GB UD-Q2_K_XL, 2-bit, 335 GB UD-IQ3_XXS, 3-bit, 395 GB + the default 192 GB 41 41.0 31.3 28.2 128 GB 34.0 40.6 26.6 22.8 96 GB 29.3 36.5 22.9 18.3 64 GB 21.0 30.6 15.2 11.7 The 128, 96 and 64 GB rows are the same PC with the rest of the RAM locked away. Prompt reading: long prompts read at up to 740 tok/s at 192 GB and 600 at 128 GB (at ~64K tokens). A short prompt starts answering in about 2 s. Benchmark: 99 hard questions, greedy, 43K token budget: Set UD-IQ2_XXS (this release) ds4 Q2 (last release) Full model, API Qwen 3.8 Flash-Next, API Knowledge (40) 12 9 13 7 Maths (14) 10 10 10 8 Reasoning (30) 25 23 25 21 Coding (10) 10 10 10 8 Hard coding (5) 0 0 1 0 Total (99) 57 52 59 44 UD-IQ2_XXS is within 2 points of the full API, never scores below ds4 Q2 on any set while running at the same speed, and used 11% fewer tokens across the run. Agreement with DeepSeek's API on the next token, at the settings that ship: Model General text Code and maths Agentic UD-IQ2_XXS 91.1% 94.5% 95.9% ds4 Q2 89.9% 93.1% 95.1% UD-Q2_K_XL 92.5% 95.3% 97.0% UD-IQ3_XXS 92.7% 95.9% 97.4% UD-IQ1_S 89.1% 91.5% 95.3% For reference, two hosts of the full model agreed 98.3% and 98.9% in my test. By that measure Unsloth IQ2 is about 20% closer to the full model than the old ds4 Q2. If you want better quality: UD-Q2_K_XL (335 GB) runs 31 tok/s at 192 GB and 26.6 at 128 GB, at 92.5% / 95.3%. UD-IQ3_XXS (395 GB plus the default) runs 28 and 22.8 at 92.7% / 95.9%. What changed since last time: First token ~40% sooner: a ~180-token prompt went from about 3.8 s to 2.3 s. Agent turns read ~12% faster , and tool calls that low-bit models write slightly wrong are now read as tool calls. Unsloth quants at full speed: the engine reads their Engram tables directly. 64 and 96 GB are real options: SSD read-ahead (+22% at 64 GB), bigger expert memory, and prompt reads in file order (18-20% faster on low RAM). 128 GB: ds4 Q2 went from 33 to 36 tok/s. Default sampling: the drafter now samples its drafts too, about +3.5% at default settings. First launch shows which model suits your RAM, and you can pick any other or use your own. A Setup tab in the chat page changes model, speed vs quality and the second GPU. Public CI: the release zips are now built by GitHub Actions, so you can see how each one was made. EXL3: 2.5 bpw is on the menu and the GPU kernel is about 2x faster. In my tests UD-Q2_K_XL was both faster and closer to the API, so I still point people there. Basic multi-GPU: 7% uplift using a 3060 + 3090. It doesn't scale well to 3-4+ GPUs, so use Project Maya for those builds. Fixes: contexts past 65,535 tokens, </think> in prose no longer ends the reasoning, no out-of-memory on the biggest files, a failed picture encoder no longer stops the model, faster prompts after a pause and many more. Support for custom model selection: just add them to the custom model folder and pick them in the web-ui Requirements: OS: Windows 10 or 11 GPU: NVIDIA RTX with 12 GB of VRAM or more, driver 580 or newer. 24 GB is the fast path, and AMD GPUs aren't supported yet RAM: 64 GB or more, 128 GB+ recommended Disk: a fast NVMe SSD, with 233-395 GB for the model (UD-IQ3_XXS also needs the default model) CPU: any x86-64 CPU, Intel or AMD. AVX2 is the fast path All of this is one PC. It needs testing on other hardware. Repo: https://github.com/musicurgy/delta-inference-engine MIT licensed. It's a Windows port of Xolotl, built on Strata. Thanks to DeepSeek for the model, Unsloth, nazeshinjite and ISTA-DASLab for the quants, and to the Xolotl and Project Maya communities, whose work and public numbers helped a lot. Claude wrote most of the code and I ran the project. If you try it, post your GPU, RAM, model and tok/s, good or bad. submitted by /u/musicurgy to r/LocalLLaMA [link] [comments]

Today we are introducing our second model generation with Inkling 2 Small. It is a Mixture-of-Experts model with 594B total parameters, 17B active , and supports image inputs and variable effort. Inkling 2 Small far surpasses our previous models’ scores on the Artificial Analysis Intelligence Index with a score of 31, reflecting gains in performance on agentic coding, vision, knowledge work, and reasoning. The increase in capabilities comes from improvements made at every step of our training process, including more efficient pre-training and a large, unified RL run. Inkling 2 Small will initially be available through Tinker, with closed weights, following the approach we outlined in a safe path to open weights . We intend to make the full weights available under an Apache 2.0 license next week after the completion of external safety testing and red-teaming. You can try Inkling 2 Small on Tinker Playground today. The model is also available for fine-tuning and sampling on Tinker; checkpoints will be downloadable with the release of Inkling 2 Small’s weights. :( Previous Inkling Small model's size is only 276B total, 12B active . Hope they release additional models Mini or Nano soon or later. submitted by /u/pmttyji to r/LocalLLaMA [link] [comments]

We introduce FireFlash , a 300B-scale model tailored for long-horizon agentic tasks. FireFlash excels as a terminal and coding agent, achieving 46.2% on Terminal-Bench 4.0 1 , putting it on par with Fable 5 and GPT-6 Sol, while costing over 30× less per task than Fable 5 at maximum effort (Figure 1). These gains also transfer to software engineering benchmarks, including DeepSWE v1.1 2 and SWE-Bench Pro Verified 3 (Table 1). While frontier labs scale reinforcement learning across thousands of GPUs, we reach the performance-cost Pareto frontier under 5 days using 64 NVIDIA B300 GPUs . Three key factors make this possible: (1) careful curation of agentic RL environments so that every task provides a reliable signal at the right difficulty; (2) an RL recipe built for long, stale, and low-precision rollouts; and (3) an optimized coding harness tailored to the model for both training and evaluation. Together, these components bring frontier efficiency to a hundred-GPU-scale cluster, and we are releasing the full recipe. Blog Post : https://agenteconomics.org/posts/agentic-rl-recipe Tweet : https://x.com/kaiwei_chang/status/2117378474654781780 Open weights : Soon submitted by /u/pmttyji to r/LocalLLaMA [link] [comments]

On ExploitGym, which tests whether models can exploit a specified vulnerability, GPT‑6 Cyber scores 48.5%, up from 32.1% for GPT‑5.6 Cyber. submitted by /u/TorturedPoet30 to r/singularity [link] [comments]

I wanted to play Portal 2 natively on Apple silicon, so we (that is, Opus 5.5 and I) built a tool that translates the Linux release’s machine code into C (about 125,000 functions) and then compiles it to native ARM64. It turns out it took only two days to get a playable room (Claude thought it would take many months). There’s no emulator, Wine or Rosetta involved. You need your own copy of Portal 2. The repo has no game files; the native app is generated on your machine. It’s playable on an iPhone 15 Pro Max with touch controls. You fire portals by tapping the back of the phone: https://youtu.be/YWX2BeMnlQI . You can also use a Bluetooth controller (tested with 8BitDo). Co-op works over LAN: a Mac hosts, and another Mac or an iPhone joins by IP address. We recently replaced OpenGL with a direct D3D9 → Metal path, built on mtld3d ( https://github.com/athei/mtld3d ). In one scene on an M3 Pro, it ran at 122 fps versus 81 with OpenGL. Numbers vary a lot by room. Claude Code wrote the code; I set the direction, tested on my devices and made the calls. I used Codex as a reviewer and for second opinions when Claude was stuck. Happy to answer questions about the project. Contributions on GitHub are welcome: https://github.com/kanapitsas/portal2-recomp submitted by /u/kanapitsas to r/ClaudeAI [link] [comments]

CCORE GLB-3D-Coder dataset is here: 3,918 rendered and tested examples across 40 domains As promised in my previous posts, I’m releasing the dataset used to train CCORE GLB-3D-Coder , my small 4B model for creating 3D assets with Three.js and GLB export. The dataset contains 3,918 examples across 40 domains , including characters, creatures, vehicles, laboratory equipment, musical instruments, architecture, machinery, nature, and everyday props. The grid shows 256 examples. Every example was tested and rendered, and its exported GLB was validated before inclusion. What’s included: Complete conversations, reasoning traces, and HTML/JavaScript responses. A rendered reference image for every example. Static assets and examples with animation and interaction. 3,501 training examples and 417 validation examples. MIT license. Dataset · Model and full weights More about what I’m planning next: Next releases . I hope this is useful for your own experiments, fine-tuning, and 3D tools. If you try it, let me know how it goes :) submitted by /u/ConfidentDinner6648 to r/LocalLLaMA [link] [comments]

A little while ago, I shared a small interactive globe I'd been building for my two boys, aged 9 and 6. I'm just a dad with no coding background, and I built the whole thing with Claude Code (Opus 5.5). It got way more attention than I ever expected. And the feedback from parents and kids has been so lovely that I'd really love for more kids to get to explore it. So I wanted to share it here too, since none of this would exist without Claude Code. Quick recap: I wanted my kids' screen time to be a bit more meaningful, so I started vibe coding a 3D globe where they could explore the world. They quickly became my entire dev team. "Dad, why isn't this in the game?" "Can you make it do this?" That hasn't stopped. Since my first post, We've shipped a lot of updates, most of them straight from their wish list and from people's feedback, and it has gotten so much better: - 🌍 830+ landmarks and sights, each one made in 3D - 🦒 470+ animals that walk, swim and fly, plus an animal dex to fill in - 🚂 180+ vehicles you can actually ride: cars, trains, ships and planes - 🌙 Real day and night, with city lights coming on after dark - 🚀 You can even fly to the Moon - 🎯 A location-guessing game, quizzes and a daily landmark puzzle - 🌐 English, Korean, Japanese, Chinese and now Indonesian A small moment that made it all worth it. Not long ago, I asked one of my boys where the Eiffel Tower is. “Spain!” he said, with total confidence. He had no idea. After just a couple of days playing with the globe, he now knows where most countries are, and honestly, his geography is now better than most adults I know. The biggest surprise: it somehow found its way into classrooms around the globe, which still blows my mind. My workflow is still pretty simple. The boys ask for something, I describe it to Claude, and we test it together. They're still my lead testers. It's called My Earth Toy, and it's completely free, with no ads. https://myearthtoy.com/ You can try it without an account, or a simple sign up for the full experience. It's still very much a work in progress, with plenty of bugs and things I want to improve. If you have curious little ones (physically or internally), I'd love for you to try it together. Your feedback means a lot. Thank you for checking it out! submitted by /u/monoloni to r/ClaudeAI [link] [comments]

This version (chk00133000) replaces the first 'final' release (chk00078000), from two months ago. I decided to train a 'few more' samples (55K more to be precise), the newer part with five critic heads where the first release had one. Measured against the 8-step teacher on the 15 test prompts at 12 sizes: cleaner fine structure — the 8- and 16-pixel grid patterns at 0.96–0.98× the teacher's, where the first release was at 1.09–1.11× less ghosting and fewer doubled edges — closer to the teacher at 10 of 12 sizes on both measures cleaner flat areas — grain at 0.99× the teacher's, down from 1.08× no over-sharpening at large sizes — fine texture at 1.01–1.04× the teacher's at 1440 px, down from 1.18–1.20× saturation matching the teacher's Full details of changes from 78K to 133K: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/blob/main/DETAILED-README.md#chk00133000-vs-chk00078000 The older versions are still kept as before under _archive/checkpoints/ . To use the new checkpoints - redownload/replace krea2_turbo_4step_rank_64_lora_comfyui.safetensors / krea2_turbo_4step_rank_64_lora.safetensors Since the initial training (than ended in September with 78K 'final' checkpoint), I have trained about a day on an H200 (on runpod in the cloud), which is where the bulk of new trained samples came from, and two more days on the RTX 3090. I've also tidied up the main model card (more sections taken out of it and moved to the detailed card for those interested). Instead the main card now includes an extra Table Of Contents like section with links to Detailed card: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA#detailed-model-card For now I will not call this another 'final' release 😀 (although I don't currently have plans to do more than the 133K training), as it is possible to train more in the future (just not soon). When I first stopped at 78K rumours were already circulating that Krea 3 would open source soon (so it felt like the right time then to 'finalise' the 4 step LoRA), and while Krea 3 is now available on paid platforms it is yet to be open sourced and most likely focused on Editing more than T2I, which makes the Krea 2 Turbo still a very desired model and my LoRAs do contribute in the more efficient use of the Krea 2 Turbo. Don't forget to also check the 'latest' of my Krea 2 Turbo step LoRAs - the 3 step one, which was updated just yesterday to 66K checkpoint: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-3step-LoRA . I might also come back to the 2 Step LoRA with more training, who knows... It is important to understand that while my 2/3/4 Step LoRAs for Krea 2 Turbo were trained for these respective steps during rendering, they for obvious reasons work really well with even more steps. There are lots of Krea Checkpoints out there that require lets say 10, or 12 or more steps to render and when paired up with my Krea 2 Turbo 2, 3 or 4 Steps LoRAs you can get either 'sooner' (with less steps) to the asked number of steps by the checkpoint authors, or to get a lot more quality per step , i.e. if you run 2 Step LoRA with lets say 6 or 8 steps, this could give you quality you'd otherwise need 12 or 16 or 20 steps without LoRA, similarly do try the 3 Step and 4 Step LoRAs at higher step counts where such is required by a third party Krea checkpoint and you will see an instant boost of quality, so find the right LoRA and steps for your favourite checkpoint, either way all of the three - 2 Step LoRA , 3 Step LoRA and this 4 Step LoRA work fine with most checkpoints (but not all, as some checkpoints go in a very different direction to what the core Krea 2 Turbo does, and after all my LoRAs were trained on the official Krea 2 Turbo). Latest 133K 4 Step LoRA and ComfyUI workflow can be downloaded as usual at my HF: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA Also don't forget that Krea 2 Turbo works very well with Nvidia PiD to get to higher resolutions quicker by rendering at lower resolutions and letting PiD take it 4x in latent space, and do so quickly and efficiently: https://huggingface.co/lvladikov/Krea2Turbo-PiD-SuperFast-HighRes (again find the right LoRA and steps to use for the main Krea 2 Turbo renders, don't be afraid to use 6 or 8 steps with either of the LoRAs since you'll be doing it at lower resolutions, prior to PiD handover, and as such these more steps would run faster anyway, so the time cost for more steps is less when using PiD). Enjoy! submitted by /u/TimeTruth2490 to r/StableDiffusion [link] [comments]

Thinking Machines Lab : Thinking Machines Lab debuts Inkling 2 Small, a MoE model with 594B total and 17B active parameters, and plans to release weights next week after safety testing — Today we are introducing our second model generation with Inkling 2 Small. It is a Mixture-of-Experts model with 594B total parameters …

We’ve released Qwen-Image-2.1 Uncensored Omni , the most capable unrestricted Qwen-Image-2.1 model we’ve built so far. The rank-128 adapter was trained on roughly 1 million images , merged into the base transformer at strength 1.0, and released in nine formats: BF16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, Q4_0, FP8, INT8 ConvRot and NVFP4. One model supports both text-to-image generation and natural-language image editing , with an emphasis on flexible poses and complex compositions. When comparing it, please use models designed for the same unrestricted use case. Restricted checkpoints operate under different constraints, and the trade-offs become obvious on the content they avoid or handle less effectively. Playground: chatrlabs.com/playground Download: Hugging Face GitHub: Code and documentation Civitai : https://civitai.red/models/3034876/qwen-image-21-uncensored-omni-quantized Feedback and comparison results are welcome. Please include your GPU, workflow and quantization. submitted by /u/metalfans to r/StableDiffusion [link] [comments]

Source submitted by /u/ResultBackground2450 to r/singularity [link] [comments]

submitted by /u/141_1337 to r/singularity [link] [comments]

submitted by /u/panchovix to r/LocalLLaMA [link] [comments]

submitted by /u/141_1337 to r/singularity [link] [comments]

MiMo V2.6 Xiaomi MiMo's mimo-v2.6 , a 9B model built for code and agent workflows, is now supported in Ollama. ollama run mimo-v2.6 AutoTrust decision models AutoTrust decision models read text and return choices, yes or no probabilities, and ratings for the questions you supply. The family includes 9b, 35b, and 27b-vl sizes — the 35b and 27b-vl sizes can also read images. ollama run autotrust-jev " A customer says their card was charged twice. " --ask " Is this a billing issue? " Note: autotrust-jev is supported on Apple silicon in this release. What's Changed ollama launch now supports Kimi Code CLI — the successor to kimi-cli — and offers to migrate existing kimi-cli setups ollama launch now fills in Pi's model list automatically so models can be picked inside Pi, and Claude Desktop's model picker shows Ollama's actual model names Recurrent and vision GGUF models can now serve more than one request at a time Qwen3 and Qwen3.5 models using long-context (YaRN) scaling Running ollama with no arguments opens the welcome launcher Full Changelog : v0.40.14...v0.40.15