Knowledge catalogue
MiniMax M2.7 is available on Ollama's cloud, and is licensed for commercial usage. Use it with OpenClaw: ollama launch openclaw --model minimax-m2.7:cloud Coding agents, such as Claude: ollama launch
This Reddit post discusses using the Ostris AI-Toolkit to train a LoRA (Low-Rank Adaptation) model targeting the 'Anima' Stable Diffusion model. The Ostris AI Toolkit is a powerful framework that allo
arXiv:2604.06212v1 Announce Type: cross Abstract: Analytical code is essential for reproducing diagnostic and prognostic prediction model research, yet code availability in the published literature re
arXiv:2603.26588v2 Announce Type: replace-cross Abstract: We present ToothCraft, a diffusion-based model for the contextual generation of tooth crowns, trained on artificially created incomplete teeth
arXiv:2508.05423v2 Announce Type: replace Abstract: Although artificial neural networks are often described as brain-inspired, their representations typically rely on continuous activations, such as t
NEWS: The Tesla Model Y was the top-selling passenger vehicle in China in March 2026, with 39,827 retail registrations, beating all EVs and ICE models. It outpaced competitors across sedans, SUVs, and
For users running Ollama on an NVIDIA RTX 4060 Ti with 8GB VRAM and 16GB system RAM, the community consensus recommends 7B–8B parameter models (such as Llama 3.1 8B, Mistral 7B, or Qwen 8B) using Q...
arXiv:2604.06628v1 Announce Type: new Abstract: A prevailing narrative in LLM post-training holds that supervised finetuning (SFT) memorizes while reinforcement learning (RL) generalizes. We revisit t
arXiv:2511.19693v3 Announce Type: replace-cross Abstract: Payment networks form the backbone of modern commerce, generating high volumes of transaction records from daily activities. Properly modeling
arXiv:2604.06644v1 Announce Type: cross Abstract: As deep learning inference is increasingly deployed in shared and cloud-based settings, a growing concern is input repurposing, in which data submitte
What if a model became the computer itself? NEW paper from Meta. (bookmark this one) What if the model wasn't just using the computer, but became the computer? New research from Meta AI and KAUST make
Building and serving models on infrastructure is a strong use case for businesses. In Google Cloud, you have the ability to design your AI infrastructure to suit your workloads. Recently, I experiment
ok i read the cyber part of the mythos model card. some thoughts. 250 'trials' across 50 crash categories but almost every full exploit is a permutation of the same 2 bugs, rediscovered from different
Seems like a good model from Meta that is still trailing the current series of releases. The most important thing to note is that it is not open weights. That was the main reason that Meta's models we
Anthropic didn't release their latest model, Claude Mythos (system card PDF), today. They have instead made it available to a very restricted set of preview partners under their newly announced Projec
http://Z.ai releases GLM-5.1, a 754B-parameter model that it says outperforms GPT-5.4 and Claude Opus 4.6 on SWE-bench Pro, available under an MIT license (@carlfranzen / VentureBeat) https://ventureb
TL;DR -- Interpretability might be way more tractable than we thought, and the guy who helped convince everyone otherwise is now saying we can understand models by directly studying neurons and their
arXiv:2610.19996v1 Announce Type: new Abstract: Large language models often produce fluent but factually incorrect output. Gray-box detectors identify such hallucinations from the token-probability st
Nemotron-3.5-Embed-1B-BF16 is a versatile embedding model developed by NVIDIA for multimodal and multilingual question-answering retrieval. The model can embed a document page in the form of an image,
https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB-GGUF https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB-LiLiCorr-GGUF NVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB is a vi
Small teams, short runway, and constant pressure to ship faster — it’s the reality we see every day among the founders we work with at Google for Startups. When you are building a company from the gro
Tessera-27B-B-CJK is a Qwen3.8-27B coding agent that thinks in compressed CJK, and it spends fewer tokens doing it. Open weights, Apache-2.0. This is our first full 'Tessera' lineup model drop! - Hugg
arXiv:2610.11132v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) equips large language models (LLMs) with specialized capabilities, but often comes at the cost of forgetting the general
https://preview.redd.it/owhrvbbiu2uh1.png?width=4096&format=png&auto=webp&s=55d816753e7adee0e1391c9f581052b4bde44da7 d1-omni-600M d1-omni-600M is a 600M parameter decision model built on LFM2.5-Encode
arXiv:2610.05397v1 Announce Type: new Abstract: Generative modeling of graph-structured data is crucial for tasks ranging from drug discovery to social network simulation. Among these models, denoisin
arXiv:2610.04433v1 Announce Type: cross Abstract: A coding agent is a language model wrapped in a harness: the system prompt, the tool set, and the context management that turn a chat model into somet
arXiv:2610.00912v1 Announce Type: new Abstract: Large language models can translate business descriptions into optimization models, but executable code may misrepresent constraints or objectives. A so
arXiv:2610.00929v1 Announce Type: cross Abstract: Models specialized for the same task converge to similar behavior, yet the parameter updates that produce it share no common coordinate system, so wei
arXiv:2604.07530v3 Announce Type: replace-cross Abstract: Scaling laws describe how language model capabilities grow with compute and data, but say nothing about how long a model matters once released
spec : support DFlash for HunyuanOCR (#28890) model : add DFlash layer-input taps for HunyuanVL DFlash speculative decoding needs the target graph to expose the residual stream entering each layer (re
https://preview.redd.it/nulsv53o8vqh1.png?width=4500&format=png&auto=webp&s=74765dbd409f4c221640f9f6000a685f6fdbb242 Spent weekend benchmarking the Splash engine (by Incoai) and extending its architec
arXiv:2609.17913v1 Announce Type: new Abstract: Face--voice association models may rely on language or gender cues in the voice rather than on speaker-specific voice characteristics, which can lead to
model : add support for HrmTextForCausalLM (DFM Mimir 1B) (#27625) model : add support for HrmTextForCausalLM (DFM Mimir 1B) HRM-Text runs two transformer stacks (low, high) in an alternating cycle ov
Hi 👋 long time lurker It’s been a few years since the last post about Horse Racing within this sub. A few years ago I was reading a popular science book and came across the story Bill Benter and the s
I can't stand kv cache quantization. Even at q8_0, I can feel the difference. But realistically, when running Qwen3.8-27B-UD-Q4_K_XL on my 32 GB GPU, I only have room for ~170k tokens (with mtp and mm
Some people says terminal bench reflects model intelligence better than the intelligent index. From the look of it, the ranking does seem to reflect how people feel about the open and closed models. F
arXiv:2609.10357v1 Announce Type: new Abstract: Time-series foundation models are evaluated almost exclusively on public archives that predate them, so a strong score cannot be separated from having s
One thing that has bothered me about LLM benchmarks for a while is that most of them are essentially snapshots. A model is evaluated, a score is published, and we tend to talk about that score as if i
arXiv:2609.04476v1 Announce Type: new Abstract: Performance modeling is central to hardware design and software optimization, yet constructing these models requires structured reasoning about computat
We all want more tokens per second but I keep seeing confusion on what to expect for given hardware. For the decoding phase (TG/s) to produce one token all the model weights and KV cache needs to be r
🦙 @ollama is used by 9 million developers and 85% of the Fortune 500, giving co-founder and CEO Jeffrey Morgan (@jmorgan) a unique view into which AI models people are actually using and how that’s ch
Integrating modern generative AI capabilities into Kotlin applications shouldn't require juggling raw HTTP clients or bridging disparate Java libraries. Today, we're excited to announce the 1.0 releas
arXiv:2608.29419v1 Announce Type: cross Abstract: Deep learning architectures are increasingly proposed for patient trajectory modeling in electronic health records (EHRs), yet their advantage over si
arXiv:2608.28439v1 Announce Type: new Abstract: One model passed our fidelity check without ever opening the datasheet. We found it while qualifying models for an internal extraction service: a struct
I keep seeing demos of AI agents building scenes in Blender through BlenderMCP, so I tried it myself. I ran both models locally for this and picked the GLM 5.3 family(Q4 quant) because videos of it do
arXiv:2608.26178v1 Announce Type: new Abstract: There is growing interest in whether language models have stable preferences, for technical, safety, and philosophical reasons. We test 20 language mode
arXiv:2608.21727v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is deployed to make models better at reasoning tasks, but its side effect on what models will di
arXiv:2608.20918v1 Announce Type: new Abstract: Organizations maintain task-specific adapters for open-weight language models, and each new base-model release forces a migration decision: retain exist
Hey guys, Inco AI shipped DFlash 2 a few days ago with a drafter for Qwen 3.8 27B and a llama.cpp PR. I built the PR and ran it against plain decoding, MTP, the n-gram lookup drafters, and my July DFl
arXiv:2608.12307v1 Announce Type: cross Abstract: Recent work on distillation transfers the capabilities of large models to smaller ones often by updating the latter's parameters, through teacher forc
North Micro Vision Instruct is a 2.4B-parameter open-weight vision-language model with native-resolution image support, released under the Apache 2.0 license. It is designed as a compact foundation fo
arXiv:2608.06564v1 Announce Type: cross Abstract: Quantization is how large language models are actually deployed, and below four bits it is known to hurt. What nobody can say is which of the model's
The intersection of medicine and AI has led to remarkable innovations. However, developers now face the thorny challenge of building robust medical AI tools that have been tested and evaluated on dive
arXiv:2608.04719v1 Announce Type: new Abstract: Agent evaluations tell us that a model picked the wrong tool, but rarely why. We introduce canary tools: diagnostic probe tools planted in an agent's Mo
I released LLM 0.32 this morning, the most significant new version of LLM since the initial launch of the project. The new version includes support for visible reasoning traces, server-side provider t
arXiv:2509.21079v2 Announce Type: replace Abstract: Foundation models have shown remarkable capabilities in various domains, but their performance on complex, multimodal engineering problems remains l
Open letters about AI development I wrote this summary of the past few weeks of open letters as a section of my sponsors-only newsletter but I've decided to share it here as well. Open Weights and Ame
arXiv:2508.02092v3 Announce Type: replace-cross Abstract: Large language models represent significant investments in computation, data, and engineering expertise, making them extraordinarily valuable
Welcome to our latest Gemini Enterprise Agent Platform deep dive, a practical walkthrough where we’ll teach you how to build real-world, production-ready agents starting from step 1. If you haven’t al
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 multil