Google has published google/tipsv1-l14, the original v1 L/14 release of TIPS, a contrastive vision-language model that produces spatially rich image features aligned with text embeddings. The L/14 variant has 304M vision parameters and 184M text parameters at 448 resolution, with an embedding dimension of 1024, and is licensed under Apache 2.0.
Google has published TIPS B/14 (v1) on Hugging Face, a contrastive vision-language model that produces spatially rich image features aligned with text embeddings. The model has 86M vision parameters and 110M text parameters at native 448 resolution, and is licensed under Apache 2.0. The release includes usage code for image and text encoding, zero-shot classification, and spatial feature visualization.
Liquid AI released 4-bit Q4_0 GGUF checkpoints for LFM2.5-230M, LFM2.5-350M, LFM2.5-1.2B-Instruct, and LFM2.5-2.6B, trained with Quantization-Aware Distillation. The company says the checkpoints recover most accuracy lost to quantization, reaching roughly 97% of their BF16 averages while keeping Q4_0 memory footprint and throughput. Benchmarks compare them against post-training quantized Q4_0 GGUFs and against Q5_K_M, Q4_K_M, and Unsloth's UD-Q4_K_XL.
AIWhy it matters: The post shows how quantization-aware distillation recovers accuracy lost in Q4_0 checkpoints, with throughput measured across four hardware backends for deployment tradeoffs.
GLM-5.3 API is now live. - Built for coding, defensive cybersecurity, and long-horizon agentic tasks - Priced the same as GLM-5.2 - Available via the official API and partner model gateways Get started: https://docs.z.ai/guides/llm/glm-5.3
@arena Try it today on MAI Playground: https://msft.it/6011azOI5
MAI-Image keeps climbing! Now #3 on @arena's image editing leaderboard. Our latest MAI-Image model is available in Private Preview on Microsoft Foundry.
Qwen3.8-27B is seeing more usage than any open model we’ve ever seen! For comparison, the previous most-liked Unsloth GGUFs were Qwen3.6-35B-A3B with 1.54K likes and DeepSeek-R1 with 1.12K. Qwen3.8-27B is truly on another level!
Z.ai's release notes announce GLM-5.3, which the company says delivers a 50% gain over GLM-5.2 on Z.ai Code Bench and reaches open-source SOTA on public benchmarks including Terminal Bench 3.0. The company also reports that GLM-5.3 matches Mythos 5 in white-box code review and vulnerability discovery, identifying 2,436 vulnerabilities in real-world targets, 1,097 of them medium- or high-severity. A separate GLM-5.3-Flash entry describes native visual capabilities and a hybrid architecture with 320B total and 18B activated parameters.
AIWhy it matters: The release notes show GLM-5.3's coding and cybersecurity gains, with a vulnerability count, letting readers compare it against Z.ai's prior GLM-5.x line and other coding models.
Qwen3.8-27B GGUF has reached 1,000 likes in less than 24 hours! ❤️ It's now the #3 trending model on Hugging Face with 1M overall downloads. Run on 17GB RAM/VRAM setups via Unsloth! Model: https://huggingface.co/unsloth/Qwen3.8-27B-GGUF GitHub: https://github.com/unslothai/unsloth
Cohere and Cohere Labs released North Small Translate 1.0 as open weights for research, a sparse Mixture-of-Experts model with 25B active and 218B total parameters. It is specialized for machine translation across 50 languages, with a 16K input and 16K output context. The chart shows a WMT26 all-languages score of 83.60, rising to 84.36 with the agentic multi-pass workflow, and the model is licensed CC BY-NC 4.0 with an acceptable use policy.
AIWhy it matters: The model card lists the benchmark score, hardware needs, and license terms, which helps readers judge whether this translation model fits their use.
Z.ai says GLM-5.3 is its most capable model for cybersecurity tasks, with CyberGym at 84.5% versus 77.2% for GLM-5.2 and ExploitBench at 54.4% versus 24.4%. Access will begin with selected security partners in controlled settings, followed by broader access and API availability, with full open weights to be published after safety evaluations are complete. The company also launched the OpenVuln initiative to help open-source maintainers audit projects and coordinate disclosure.
An initial group of partners is now offering GLM-5.3-powered services through our official service, with its safeguards and usage policies in place. We’re expanding partner access through a consistent and responsible process and will share updates publicly.
Z.ai says GLM-5.3 is available now through GLM Coding Plan and ZCode. API access and open weights will follow in stages after rigorous safety evaluations.
MathForm-8B is an open-source autoformalization model from OpenBMB that translates natural-language mathematical statements into Lean 4. It was trained on FormalVerse through supervised fine-tuning, then reinforcement learning using Lean compilation and semantic-consistency feedback. The model is available on Hugging Face under Apache License 2.0 and can be served with Transformers, vLLM, or SGLang, using a recommended max_new_tokens of 16384.
LongCat-2.0 is now live on the @NousResearch Portal and free to try with Hermes Agent for one week! 🐱
Oh yes, and Flash 3.7 is also lightning fast! ⚡️
Learn more: https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-3-7-flash/
Google has made Gemini 3.7 Flash available in Google Antigravity, described as its most intelligent workhorse model yet for coding and agents. Users can download or upgrade Antigravity to try the model. The author, Varun Mohan, says the model brings a big capability improvement at half the API cost.
Learn more: https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-3-7-flash/
Google AI Developers announced Gemini 3.7 Flash as its most intelligent workhorse model yet for coding and agents, citing higher instruction adherence, first-pass code accuracy, and high-quality agentic execution. The post shows the model building a complex 3D web game in Antigravity, covering Three.js engine logic, asset orchestration with PBR textures and Nano Banana sprite sheets, and procedural sound effects.
AIWhy it matters: The post shows a concrete build workflow across engine logic, assets, and audio, which helps readers judge how the model handles multi-step agentic coding.
Google DeepMind has released Gemini 3.7 Flash, which the post says is stronger for coding, knowledge work, and web development. Its introductory price is half the original cost of Gemini 3.6 Flash.
AIWhy it matters: The post names concrete upgrade areas and a price change against the prior version, which helps readers compare it with earlier Flash releases.
Google launches Gemini 3.7 Flash, its latest Flash model for coding and agentic workflows, with an introductory price at half the original cost of 3.6 Flash. The post reports gains from 3.5 to 3.7 Flash, including DeepSWE v1.1 rising from 37.0% to 65.3%, Code Arena Elo from 1506 to 1588, and AutomationBench from 13.4% to 30.4%.
AIWhy it matters: The post pairs a launch with specific before-and-after benchmark gains and an introductory price, letting readers weigh capability against cost for coding and agent work.
Koray Kavukcuoglu announced Gemini 3.7 Flash, saying it brings a large jump in agentic performance and coding accuracy. To demonstrate, a three-agent team was set up to autonomously train a robotics control model from scratch. The full introduction is linked on the Google blog.
ByteDance has released Bernini-Diffusers-v2 on Hugging Face, a video generation and editing pipeline combining a Qwen2.5-VL planner with Wan2.2 diffusion components. The model card recommends it over Bernini-R for complex requests needing stronger instruction following and multi-step semantic planning. Code and weights are available under Apache License 2.0.
DeepSeek announced the launch of DeepSeek-V4-Pro, citing major Agent upgrades and flexible reasoning effort settings of low, high, and max for V4-Pro and V4-Flash. The model supports the native OpenAI Responses API and is optimized for Codex with one-click setup. V4-Pro is available on the app and web through Expert Mode and via API, with model names unchanged.
DeepSeek has released DeepSeek-V4-Pro-0813 as the official version superseding the V4-Pro preview, built on the preview structure with a DSpark speculative decoding module. The model scores higher than the preview on the listed benchmarks, including Terminal Bench 2.1 at 87.9 and DeepSWE at 62.7, and the weights are under the MIT License.
AIWhy it matters: The release reports agent benchmark gains over the preview and lists vLLM and SGLang setup, useful for judging deployment cost and fit.
MiniMax introduces Music 3.0, a music generation model that composes, arranges, performs, and produces a complete song from a creative concept and optional lyrics. The post describes an eight-layer RVQ tokenizer, a Hybrid-LM pairing an 8B Global LLM with a 0.6B Local LLM, and a flow-matching and Flow-VAE audio renderer. It says songs can run up to five minutes and that the model focuses on creative intent, arrangement, and vocal naturalness.
AIWhy it matters: The post explains how the model's pipeline targets structure, acoustic detail, and vocal realism, which helps readers judge where open-weights music generation stands.
Liquid AI has released LFM2.5-2.6B, a model optimized to run agentic workflows entirely on-device without cloud escalation. The company said it is designed for high-volume agentic tasks and chained workflows while staying on-device. Separately, Liquid AI and MacPaw announced a long-term partnership to co-develop local AI technology for Mac, with LFMs running on Apple silicon through MacPaw's Elix inference engine.
The 1.5T that could! Bigger and even better models on the horizon.
Grok 4.6 is released as a significant improvement over Grok 4.5 at the same price, according to the announcement. The author says it is significantly better at difficult tasks and knowledge work, combining Opus-class intelligence and polish with low cost and high speed. A comparison table shows Grok 4.6 High scoring 61 on the AA Intelligence Index, versus 56 for Grok 4.5 High, and 1753 on GDPval-AA v2, versus 1526.
Liquid AI released LFM2.5-VL-3B, an open-weight 3B vision-language model that it says rivals models twice its size while running faster on CPU and GPU. Benchmarks show large gains over LFM2-VL-3B, including ScreenSpot-v2 averaging 80.7, RefCOCO precision@1 rising from 57.1 to 87.9, and ToolSandbox rising from 26.4 to 59.5. The model is available on Hugging Face and decodes 228 tokens/s on an Apple M5 Max.
AIWhy it matters: The post pairs benchmark gains with on-device and GPU throughput figures, showing how a 3B vision model trades size against speed and accuracy.
LiquidAI has released LFM2.5-VL-3B, a 3B-parameter multimodal model that processes text and images and is built on the LFM2.5-2.6B language model with a SigLIP2 NaFlex vision encoder. It runs at 228 tokens/s on an Apple M5 Max and 116 tokens/s on an AMD Ryzen AI Max+ 395 in under 3.3 GB of memory, with a 32,768-token context length. The model is available in native, GGUF, ONNX and MLX formats on Hugging Face.
What a tremendous milestone - thanks to all 1B+ people globally who use the Gemini app to help them throughout their day and to our entire team for great work!
Meta announced it is opening the weights for Muse Glimmer, a 30B parameter dense model that can run locally. Muse Spark 1.2, described as its latest foundation model, will have its weights released soon. The author's interview with Mark Zuckerberg quotes him saying Llama 4 fell short of the trajectory he wanted and that the lab was rebuilt.
Nemotron 3.5 Lightning: Same architecture as 3.0 Nano, with added speculative decoding, and with the intelligence of 3.0 Super. ⚡️⚡️⚡️
Liquid AI released LFM2.5-2.6B-DSpark, a 327.7M-parameter speculative-decoding draft model for its LFM2.5-2.6B target, on Hugging Face. In SGLang on a single H100 with batch size 1, mean decoding throughput rises from 323 to 864 tokens per second, about 2.67x, and on an Apple M4 Max via Metal it rises from 61 to 139 tokens per second, about 2.27x. Because the target verifies every proposed token, the output matches what LFM2.5-2.6B would generate alone.
Liquid AI released LFM2.5-8B-A1B-DSpark, a 327.7M-parameter speculative-decoding draft model for its LFM2.5-8B-A1B target. In SGLang on one H100 with batch size 1, mean accepted tokens per step reached 7.21 across five benchmarks, and decoding ran about 2.6× faster. The model also runs on Apple silicon through the Metal backend, with a 1.18× mean speedup on an M4 Max.
Liquid AI released LFM2.5-1.2B-Instruct-DSpark, a 295.7M-parameter speculative-decoding draft model for the LFM2.5-1.2B-Instruct target on Hugging Face. On an H100 it averages 4.81 accepted tokens per step and runs about 2.10x faster across benchmarks, with about 2x speedup in SGLang and on-device Apple silicon support via Metal.
Thank you Mark, Alex and the whole Meta team for your contributions to open weight AI.
Cohere has released North Micro Vision Instruct, a 2.4B-parameter open-weight vision-language model under the Apache 2.0 license, on Hugging Face. The model processes images at native resolution and handles visual question answering, captioning, grounding, OCR, and document understanding across English, German, French, Spanish, Italian, Portuguese, Hindi, Japanese, Korean, Chinese, and Arabic. It has a 128K-token language backbone context window, but its validated multimodal range is up to 8K tokens.