Model releases

Krea 2 fine-tune places comics and text exactly with coordinate boxes

Jimmy Carter has released a fine-tune of Krea 2 that makes layout controllable: multi-panel comics, speech bubbles in the right panel, consistent characters across images and text exactly where you want it. It's driven by a compact layout language with coordinate boxes.

A fine-tune of the image model Krea 2 has appeared on Hugging Face at jimmycarter/krea2-turbo-bbox. It's meant to make layout controllable: multi-panel comics, speech bubbles that land inside their panel, characters whose look stays the same across panels, and text exactly where you want it. The release is the final version (epoch-20-step-104548) after 20 training epochs. An epoch is one full pass through the …

• The jimmycarter/krea2-turbo-bbox model is a Krea 2 fine-tune for layout control, released as the final version after 20 epochs.
• There's a Turbo variant (8 steps, no CFG) and a Raw variant (50 steps, CFG 5.5).
• Each variant comes as a fine-tune plus two EMA weight sets (decay 0.9995 and 0.99975), six checkpoints in total.

Qwen-Image-2.1: Alibaba's image model adds true transparency, but a stricter license

Alibaba has released Qwen-Image-2.1, an image model for generating and editing pictures that also runs locally. It supports true transparent backgrounds, high resolutions and up to 10 reference images. The catch: the license only allows research and personal testing.

Alibaba's AI group has released a new image model, Qwen-Image-2.1. According to heise, it came out on September 20, 2026, and you can download it for free, including via Hugging Face. Hugging Face also hosts an online demo (a Space) that you can try out. It already has 484 likes. The model creates new images from text descriptions and edits existing images, both in a single model.

• According to heise, Qwen-Image-2.1 was released on September 20, 2026. The weights can be downloaded from Hugging Face, among other places.
• One model handles both text-to-image generation and image editing.
• Features: true transparent backgrounds, high resolutions, targeted edits to individual image regions and up to 10 reference images.

Nvidia's open Nemotron 3.5 Transcribe handles transcription, translation, speakers

Nvidia has released Nemotron 3.5 Transcribe, an open speech model you steer with text prompts. It handles transcription, translation, speaker labeling and questions about recordings. Nvidia also ships the Granary v2 dataset and tools for adapting the model to fields like medicine.

On October 28, 2026, Nvidia released Nemotron 3.5 Transcribe, an open-weights speech model available on Hugging Face and ModelScope. It turns speech recordings into text, but it does more than plain transcription. It labels speakers (diarization), translates, and answers questions about what's in a recording. You control it with text prompts, much like a chatbot. Nvidia is also releasing the Granary v2 training …

• The model transcribes 40 languages, translates 31 of them to or from English, and labels speakers in 13 languages.
• It handles recordings of up to one hour with up to eight speakers. Real-time streaming is not supported.
• Of 30B parameters, only about 4B are active per token (Mamba-Transformer MoE).

Clover-35B-A3B: Diffusion language model beats Qwen3.6 at agent tasks, Apache 2.0

DiffuSpace, a team linked to the Dream models, has released Clover-35B-A3B-Preview as an open diffusion language model. It reportedly beats the equally sized Qwen3.6-35B-A3B on agent benchmarks and generates much faster. The weights are available under the Apache license.

The DiffuSpace team released its first model on October 28, 2026: Clover-35B-A3B-Preview. It is a diffusion language model. Common language models generate text one word at a time (more precisely, one token at a time). A diffusion model produces several tokens at once by gradually cleaning up an initially "noisy" block of text. That is supposed to be much faster. DiffuSpace comes from the circle around the NLP lab …

• Clover-35B-A3B-Preview is a mixture-of-experts diffusion model with 35B total and 3B active parameters, a thinking mode and 128K context.
• According to the maker: SWE-bench Verified 73.6, Terminal-Bench 2.1 51.7, τ³ Overall 65.3, GPQA Diamond 83.8.
• Speed (maker's figures): 680 tokens/s on a GB300, 515 tokens/s on an H200, versus 365 tokens/s for Qwen with DFlash2.

Liquid AI's LFM3: tiny 2B models run in your browser via WebGPU

Liquid AI has released the LFM3 model family on Hugging Face, with text, vision, audio and omni variants. A demo shows the 2-billion-parameter LFM3-VL-2B describing video in real time right in your browser, with no server at all.

The company Liquid AI has published a collection called LFM3 on Hugging Face, the largest platform for openly available AI models. It includes base models and post-trained variants (fine-tuned for tasks such as dialogue or image description). The collection was presented on Reddit, in the r/LocalLLaMA forum, together with a demo: the LFM3-VL-2B model describes video in real time and runs entirely locally in the web …

• Liquid AI's LFM3 collection on Hugging Face includes text, vision, audio and omni models, each with around 2 billion parameters.
• LFM3-VL-2B processes images and text and, according to a demo by Xenova (Hugging Face), runs locally in the browser via WebGPU.
• The demo describes video in real time (video captioning). The post appeared on r/LocalLLaMA on 10/27/2026.

Meta releases Muse Glimmer, an open 30B model you can run at home

Meta has released Muse Glimmer, an open agent model with 30 billion parameters that runs on a Mac or a PC with a single GPU. The weights are under Apache 2.0. Zuckerberg is also pushing for open AI and calling for fewer US hurdles.

On Monday, Meta unveiled Muse Glimmer, an open-weight model for AI agents. "Open weight" means the trained weights – roughly speaking, the model's learned knowledge – can be downloaded, customized and run locally instead of in the cloud. Glimmer is much smaller than the top models from OpenAI or Anthropic and is designed to run on a Mac or PC with a single GPU. Meta also announced that it will soon release an open …

• According to TechCrunch, Muse Glimmer has 30 billion parameters and is released under the Apache 2.0 license.
• Meta says the model runs on a Mac or PC with a single consumer GPU.
• It understands text and images and was trained on more than 100 languages.

Liquid AI LFM3: Tiny 2B models for speech, vision and video land in Ollama

Three new models from Liquid AI's LFM3 family have appeared in the Ollama library: Audio, Omni and Vision-Language, each around 2 billion parameters. They hear, see and speak locally on laptops and edge devices, support German, and ship under the LFM Open License v1.0.

On October 27, 2026, three new models from Liquid AI's LFM3 family appeared in the Ollama library: lfm3-audio, lfm3-omni and lfm3-vl. At around 2 billion parameters each, all three are very small. Parameters are, roughly speaking, the dials a model tuned during training. They're meant to run directly on your device, meaning laptops, phones and other edge hardware (edge means the computation happens locally, not in …

• LFM3-Audio-2B takes text or audio and replies with text, speech or both; speech output runs at 24 kHz.
• LFM3-Omni-2B (2.17B parameters) understands text, images, video and audio in a single model. According to the maker, it replaces separate models for speech recognition, image understanding, language modeling and speech synthesis.
• LFM3-VL-2B (1.92B parameters) offers a Thinking mode for the first time and a 128K-token context window; video support is listed as "soon."

OXR: Open 1.3B document parser tops three benchmarks, claims its maker

The team behind Chinese startup SoMark has released OXR, an open-weights model with 1.3 billion parameters that turns PDFs and document images into Markdown or JSON. According to the maker, it ranks first on OmniDocBench, olmOCR-Bench and DocParsingBench, and runs faster than PaddleOCR-VL-1.6 and MinerU2.5-Pro.

The team behind the document parser SoMark, a startup from China, has open-sourced a model called OXR. The name stands for "Optical Everything Recognition", an extension of OCR (text recognition) beyond plain text. The main model has 1.3 billion parameters (the settings learned during training), which makes it small. It converts PDFs and document images into Markdown or JSON, keeps layout coordinates and recognizes …

• OXR (Optical Everything Recognition) has 1.3 billion parameters and recognizes 13 types of document elements, including tables, formulas, chemical structures and code.
• According to the maker, OXR scores 97.18 (OmniDocBench), 87.84 (olmOCR-Bench) and 87.76 (DocParsingBench), ranking first on each.
• On an RTX PRO 6000 Blackwell (96 GB), OXR needed about 101 s for 100 pages, PaddleOCR-VL-1.6 about 145 s and MinerU2.5-Pro about 255 s.

SenseNova 6.8 Code2Video: Open 27B model writes code that renders video

SenseNova has released an open 27-billion-parameter model that doesn't generate video directly. Instead, it writes executable code, renders it and revises it based on your feedback. It suits motion design and explainer videos best. It can't do photorealistic footage.

The OpenSenseNova project has released SenseNova 6.8 Code2Video, an open language model with 27 billion parameters (parameters are, roughly speaking, the dials the model adjusted during training). It is built on Qwen3.8-27B and fine-tuned from it. Unlike classic video models, it doesn't generate video pixel by pixel. It writes executable code that computes the frames. Weights, an accompanying skill (a bundle of …

• SenseNova 6.8 Code2Video is a fine-tuned version of Qwen3.8-27B with 27 billion parameters and a 512K context window.
• The model creates videos as code, renders them, checks the frames and revises them. According to the developers, runs last over 10 hours and over 2,000 turns.
• Weights, a 'code-video' skill, prompt templates and complete production trajectories are on GitHub and Hugging Face. According to a Reddit post, INT4 and INT8 weights are planned for later.

Baidu releases Unlimited OCR 2 for documents hundreds of pages long

Baidu has released Unlimited OCR 2, a 3.34-billion-parameter OCR model under the MIT license. It reads books, reports and business documents running to hundreds of pages in a single request. A sparse attention method called SR-SWA makes that possible by keeping the compute cost per step almost constant.

Baidu has released Unlimited OCR 2, an open-source text recognition model (OCR, short for Optical Character Recognition) for very long documents. The model has 3.34 billion parameters, which are, roughly speaking, the dials it adjusted during training. It converts ordered page images into structured text: running text, tables, formulas, figures, headings and reading order. Books, annual reports or government filings …

• Unlimited OCR 2 has 3.34 billion parameters and a maximum position length of 131,072 tokens. It is released under the MIT license.
• The BF16 weights are about 6.7 GB. Baidu tested on an NVIDIA A800 with 80 GB.
• On MarathonDocBench (hundreds of pages), the model scores 77.1 points according to Baidu. The next-best model, Kimi-K3, scores just 33.7.

Kandinsky 6.0 Video: open models generate video with matching sound

Kandinsky Lab has released Kandinsky 6.0 Video: two models (29 and 3 billion parameters) that generate video complete with dialogue, music and sound effects. Code and weights are available under the MIT license, and the small variant is meant to run on gaming GPUs.

In early October 2026, Kandinsky Lab released Kandinsky 6.0 Video, a model family that generates video and sound together. There are two variants: Kandinsky 6.0 Video Pro with 29 billion parameters (parameters are, roughly speaking, the dials a model learns during training) and Kandinsky 6.0 Video Lite with 3 billion parameters. Code and model weights are released under the MIT license. That means anyone can use …

• Kandinsky 6.0 Video comes as Pro (29B parameters) and Lite (3B parameters); code and weights are under the MIT license.
• The models generate clips of up to five seconds with synchronized sound (44 kHz) and lip sync from text or a starting image.
• Base generation runs at low resolution, and a super-resolution module upscales to HD or Full HD.

Kolibri: Aleph Alpha's open German-English MoE model with 78B parameters

Kolibri is an English-German mixture-of-experts model with 78.1B parameters, of which only 3.46B are active per token. It's available as open weights under Apache 2.0 and is aimed at sovereign deployments in areas such as public administration, industry and aviation. According to the report, it sits on the Pareto frontier for quality and operating cost.

Kolibri is an English-German language model that, according to its technical report, is being released as open weights under the Apache 2.0 license. Per alphaXiv's AI summary, Aleph Alpha is behind the report. The model is built for "sovereign" and specialized use, with a focus on German and regulated sectors such as public administration, industry and aviation. The authors stress that they control the entire …

• Kolibri is a mixture-of-experts transformer with 78.1B total and 3.46B active parameters (4.4%), released as open weights under Apache 2.0.
• It was trained on 24 trillion tokens. German makes up over 20% of the data, supplemented by more than 2 trillion German tokens from a web pipeline and synthetic data.
• Post-training uses SFT and reinforcement learning on over 1.2 million internally curated tasks.

Tencent's Hy-SimulMT: Open Chinese–English live interpreter with 3-second lag

Tencent has released Hy-SimulMT, an open model that translates spoken Chinese and English live, while the person is still talking. Tencent says latency is around three seconds and quality matches experienced interpreters. The weights are available under Apache-2.0, but all benchmarks come from Tencent itself.

Tencent Hunyuan has released Hy-SimulMT, a model for simultaneous translation of speech between Chinese and English, in both directions. "Simultaneous" means the model takes in audio continuously and translates while the person is still speaking. Think of an interpreter in a conference booth, not a translator who waits for the whole sentence. It produces translated text. With a connected speech-output service, it …

• Hy-SimulMT is a simultaneous translation model from Tencent Hunyuan for speech between Chinese and English, in both directions. According to the model card, it was released on October 26, 2026 (OrcaRouter says the weights appeared as early as October 22).
• The model is a Mixture-of-Experts with about 30.4B parameters (A3B; by OrcaRouter's calculation, roughly 3B active per token) under the Apache-2.0 license.
• On the RealSI long-audio test, Tencent reports a StreamLAAL of about 3 seconds, the lowest in the comparison.

Maple-Preview: Ternary 20B MoE runs fast on CPUs and small GPUs

Deepgrove has released "Maple-Preview" as GGUF, a ternary reasoning model with 20B parameters but only about 1B active. It weighs in under 6 GiB and hits over 250 tokens per second on an Apple M5 Pro CPU. A second ternary MoE release reportedly manages 368K context on a 12 GB GPU.

The Deepgrove team has published the Maple-Preview-GGUF collection of files on Hugging Face. Maple-Preview is a reasoning model (it "thinks" in intermediate steps before answering) with 20 billion parameters, of which only about 1 billion are active per token (hence the name 20B-A1B). According to the authors, it was designed from the start to run efficiently on your own device, and it is released under the MIT …

• Deepgrove's Maple-Preview is a 20B-A1B reasoning MoE model (20B parameters total, about 1B active) under the MIT license.
• The GGUF variants range from 4.64 to 5.91 GiB (TQ1_0 or TQ2_0, output layer in Q4_K or FP16).
• On an M5 Pro, CPU only (16 threads), the fastest variant reaches 252.74 tokens/s for generation and 618.57 tokens/s for prompt processing.

OmniVoice: Free speech model clones voices in over 600 languages

OmniVoice is a free text-to-speech model that can recreate a voice from just a few seconds of audio and supports over 600 languages. Alternatively, you can design a voice from scratch by choosing gender, age and pitch. You can run it locally in your browser.

OmniVoice is a free, open-source text-to-speech (TTS) model, an AI model that turns written text into spoken language. The project is on GitHub at k2-fsa/OmniVoice. The Japanese site GIGAZINE presented it and tried it out locally on Windows 11. According to the source, it supports over 600 languages and can recreate voices from short audio samples or generate new ones from traits such as gender, age and pitch.

• According to the source, OmniVoice supports over 600 languages; the demo page offers audio samples in 102 languages, including Japanese.
• For zero-shot voice cloning, a reference recording of 3 to 10 seconds is enough, with no additional training.
• In "Voice Design" mode, you define a voice without any reference audio by choosing gender, age and pitch.

Lure-TTS and Lure Turbo: 600M Speech Models With Tags and Voice Cloning

A developer used reinforcement learning and distillation to turn Qwen3-TTS (0.6B parameters) into Lure and Lure Turbo. The models understand tags like (laugh), follow direction prompts and clone voices. Turbo runs about 4x faster than real time. The author says the whole training run cost under $200.

The developer "smirkishere" has presented two small text-to-speech (TTS) models on r/LocalLLaMA: Lure and Lure Turbo. Both are fine-tunes of Qwen3-TTS-12Hz-0.6B-Base, a model with about 600 million parameters (parameters are, roughly speaking, the dials a model learns during training). They can speak text with embedded tags like (laugh), follow direction prompts such as "warm, amused" and clone voices from a …

• Lure and Lure Turbo are based on Qwen3-TTS-12Hz-0.6B-Base and support inline tags like (laugh), direction prompts and voice cloning.
• The author reports a real-time factor (RTF) of 0.14 for Turbo, versus 0.195 for the 5B model Fish S2 Pro, at the same speaker similarity (ECAPA 0.699).
• Turbo replaces the 15-step code predictor with a 3-pass head (V2s) and is trained with on-policy distillation.