2026-10-12

Now merged. Update your llama if you haven't done so yet. Optimal draft-n-max / draft-p-min seem to be in line with greedy sampling. Main gain seems to be on prose generation. Tests above ran with thinking off, ngram-mod off.

OrcaRouter released OrcaCyber Zero 1.5, a gated cybersecurity model with a 1M-token context window. It reports 100% on Cybench and 95.8% on an evaluable CVE-Bench subset, priced at $3.00 / $7.50 per 1M tokens. The post OrcaRouter Releases OrcaCyber Zero 1.5 Cybersecurity Model With 1M Context appeared first on MarkTechPost .

AesCode generates information-rich visual artifacts such as slides, posters, and dashboards as HTML/CSS. The output remains structured, editable, and verifiable , but the task poses a distinct challenge: code models cannot see how layout, hierarchy, and color come together on the canvas. Image generators offer the opposite strength. They compose visually compelling pages but often misrender text, numbers, and logical relationships. AesCode uses an image generated from the same prompt as an aesth

Hey everyone, it has been over 1 year since my last post on this subreddit about my fork of filebrowser. Since then, the OG filebrowser github has been archived and my fork has hit both v1.0.0-stable -- and now v2.0.0-stable! It's called FileBrowser Quantum -- I have been working on this repo for almost 4 years and is still actively and officially maintained by myself and Kevin. We both handle issues and make changes nearly every day on the repository. The vast majority of code is unique to my r

I stumbled across this as I was currently having glm5.3 flash run only around 10tok/s basically unusable. With Maya i am running 30 tok/s now. GLM5.3 feels even with this low quant like a much more enjoyable model than qwen 3.8 flash next so far. Still testing . This is very exciting allowing many to run GLM5.3 flash. Project Maya A 321-billion-parameter AI model on your own GPU GLM-5.3-Flash, private and fast · one NVIDIA GPU or up to 16 · AMD and Windows (experimental) · chat, pictures, OpenAI

Dense correspondence matching has historically been bounded by simplifying spatio-temporal priors, such as smooth motion and rigid geometry. While effective for classical tasks, these assumptions break down in image editing and reference-guided generation (IEG), where transformations can preserve visual identity while breaking physical continuity. To establish identity-preserving correspondence across such transformations, we introduce FreeMatching, a generalizable framework combining generative

Multimodal large language models (MLLMs) are rapidly evolving toward continuous audio--visual reasoning, creating an urgent need for evaluations that expose their capability limits. Audio--visual captioning is an ideal diagnostic task, yet current benchmarks face a coupled trade-off: whole-caption scores provide coverage without localization, local probes provide localization without coverage, and unconstrained LLM judges introduce instability. We introduce OmniCapBench (Omni-Video Caption Bench

Two questions? Can a local model like Qwen 3.8 27B reverse engineer games and programs? If not, is there a fine tuned model that can?