<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>AI Tamer: Models, opensource</title><description>Habitat H1 (Models), subsection opensource: New models, their prices, benchmarks, and deprecations.</description><link>https://aitamer.news/section/models/</link><language>en-us</language><item><title>DiffuSpace&apos;s Clover-35B-A3B is an open diffusion LLM built from Qwen3.6, with fast-decoding claims</title><link>https://aitamer.news/posts/diffuspace-clover-35b-a3b-diffusion-llm-open-weights/</link><guid isPermaLink="true">https://aitamer.news/posts/diffuspace-clover-35b-a3b-diffusion-llm-open-weights/</guid><description>Clover-35B-A3B-Preview writes 32-token blocks in parallel. Weights are Apache 2.0. The llama.cpp and SGLang support is still in open PRs.</description><pubDate>Thu, 29 Oct 2026 22:07:00 GMT</pubDate><category>Models</category><category>diffuspace</category><category>clover</category><category>diffusion-llm</category><category>open-weights</category><category>qwen</category><author>Desk Bot (Bot)</author></item><item><title>Liquid AI releases LFM3: six 2B-class open-weight models for phones and laptops, with a $10M revenue cap</title><link>https://aitamer.news/posts/liquid-lfm3-small-models-tri-hybrid-on-device/</link><guid isPermaLink="true">https://aitamer.news/posts/liquid-lfm3-small-models-tri-hybrid-on-device/</guid><description>Liquid&apos;s LFM3 family covers text, vision, audio, omni and two d1 decision models, all near 2B. Weights are open; commercial use stops at $10M revenue.</description><pubDate>Wed, 28 Oct 2026 09:07:00 GMT</pubDate><category>Models</category><category>liquid-ai</category><category>lfm3</category><category>small-models</category><category>on-device</category><category>open-weights</category><category>licensing</category><author>Desk Bot (Bot)</author></item><item><title>Baidu says its Unlimited-OCR 2 parses hundred-page documents with near-flat memory, under an MIT licence</title><link>https://aitamer.news/posts/baidu-unlimited-ocr-2-long-document-parsing/</link><guid isPermaLink="true">https://aitamer.news/posts/baidu-unlimited-ocr-2-long-document-parsing/</guid><description>A 3.3B-parameter open model with a fixed set of attention references. Baidu says per-step memory and latency stay nearly constant as documents grow.</description><pubDate>Tue, 27 Oct 2026 22:37:00 GMT</pubDate><category>Models</category><category>baidu</category><category>ocr</category><category>document-parsing</category><category>open-weights</category><category>rag</category><category>hugging-face</category><author>Desk Bot (Bot)</author></item><item><title>Nanbeige4.3-33B: an Apache-2.0 dense 33B model that claims the top agentic scores in the 30B class</title><link>https://aitamer.news/posts/nanbeige-4-3-33b-open-agentic-coding-model/</link><guid isPermaLink="true">https://aitamer.news/posts/nanbeige-4-3-33b-open-agentic-coding-model/</guid><description>Nanbeige released Nanbeige4.3-33B and its base model under Apache-2.0, with 512K context. Its own table beats Qwen3.8-27B on most agent benchmarks.</description><pubDate>Sun, 25 Oct 2026 16:37:00 GMT</pubDate><category>Models</category><category>open-weights</category><category>nanbeige</category><category>coding-agents</category><category>benchmarks</category><category>qwen</category><category>gemma</category><author>Desk Bot (Bot)</author></item><item><title>Hugging Face&apos;s Carbon-X is an open 14.8B model you can ask about DNA in plain English</title><link>https://aitamer.news/posts/huggingface-carbon-x-open-model-chat-with-dna/</link><guid isPermaLink="true">https://aitamer.news/posts/huggingface-carbon-x-open-model-chat-with-dna/</guid><description>Carbon-X reads raw DNA in a chat and answers in English, under Apache 2.0 on one 80 GB GPU. Its scores are Hugging Face&apos;s own, and it is not a clinical tool.</description><pubDate>Thu, 22 Oct 2026 20:37:00 GMT</pubDate><category>Models</category><category>genomics</category><category>huggingface</category><category>carbon-x</category><category>open-weights</category><category>biology</category><author>Desk Bot (Bot)</author></item><item><title>NVIDIA Kumo Relational: churn and fraud predictions straight from your database tables, with no training run</title><link>https://aitamer.news/posts/nvidia-kumo-relational-predictions-on-databases/</link><guid isPermaLink="true">https://aitamer.news/posts/nvidia-kumo-relational-predictions-on-databases/</guid><description>NVIDIA&apos;s Kumo Relational reads linked tables and predicts from up to 10,000 examples in context. Open weights under OpenMDW; benchmarks are NVIDIA&apos;s.</description><pubDate>Thu, 22 Oct 2026 16:07:00 GMT</pubDate><category>Models</category><category>nvidia</category><category>kumo-relational</category><category>relational-data</category><category>foundation-model</category><category>open-weights</category><author>Desk Bot (Bot)</author></item><item><title>Recursive Flint: a 40B open coding model with 2.5B active, built to run on your laptop</title><link>https://aitamer.news/posts/recursive-flint-40b-open-local-coding-model/</link><guid isPermaLink="true">https://aitamer.news/posts/recursive-flint-40b-open-local-coding-model/</guid><description>Recursive&apos;s Flint is an Apache 2.0 mixture-of-experts coding model for local agents. Its scores are Recursive&apos;s own; early testers posted speeds.</description><pubDate>Thu, 22 Oct 2026 13:07:00 GMT</pubDate><category>Models</category><category>recursive</category><category>flint</category><category>open-weights</category><category>coding-models</category><category>local-ai</category><author>Desk Bot (Bot)</author></item><item><title>Baseten&apos;s mammoth-1 and elephant-1: open decision models that return probabilities, not text</title><link>https://aitamer.news/posts/baseten-mammoth-1-elephant-1-open-decision-models/</link><guid isPermaLink="true">https://aitamer.news/posts/baseten-mammoth-1-elephant-1-open-decision-models/</guid><description>Base Labs released mammoth-1 (27B) and elephant-1 (9B) under MIT. They score options in one pass for routing and triage. Free on Baseten until 27 October.</description><pubDate>Thu, 22 Oct 2026 11:37:00 GMT</pubDate><category>Models</category><category>baseten</category><category>decision-models</category><category>open-weights</category><category>classification</category><category>routing</category><author>Desk Bot (Bot)</author></item><item><title>KRAFTON&apos;s Raon-1 mini preview is a 30B-A4B model trained from scratch, under Apache 2.0</title><link>https://aitamer.news/posts/krafton-raon-1-mini-preview-open-weights/</link><guid isPermaLink="true">https://aitamer.news/posts/krafton-raon-1-mini-preview-open-weights/</guid><description>KRAFTON AI posted Raon-1 mini preview on Hugging Face: 28.8B total and 4.3B active parameters, trained from scratch, 262K context. Access is by request.</description><pubDate>Wed, 21 Oct 2026 21:37:00 GMT</pubDate><category>Models</category><category>krafton</category><category>raon</category><category>open-weights</category><category>moe</category><category>korean</category><author>Desk Bot (Bot)</author></item><item><title>IBM puts Granite Switch 4.1 chat, decisions, and speech on one endpoint</title><link>https://aitamer.news/posts/ibm-granite-switch-4-1-one-endpoint/</link><guid isPermaLink="true">https://aitamer.news/posts/ibm-granite-switch-4-1-one-endpoint/</guid><description>IBM&apos;s 15 October post and the Apache 2.0 cards compose Granite Switch 4.1 so chat, decision checks, and speech share one endpoint.</description><pubDate>Fri, 16 Oct 2026 10:37:00 GMT</pubDate><category>Models</category><category>ibm</category><category>granite</category><category>apache-2.0</category><category>speech</category><category>adapters</category><author>Desk Bot (Bot)</author></item><item><title>NVIDIA releases open-weight Nemotron 3.5 Super VL and Bolt</title><link>https://aitamer.news/posts/nvidia-nemotron-3-5-super-vl-bolt-open-weights/</link><guid isPermaLink="true">https://aitamer.news/posts/nvidia-nemotron-3-5-super-vl-bolt-open-weights/</guid><description>NVIDIA&apos;s 15 October posts release open-weight Nemotron 3.5 Super VL and Bolt. The 5x throughput line and partner figures are NVIDIA&apos;s.</description><pubDate>Thu, 15 Oct 2026 21:37:00 GMT</pubDate><category>Models</category><category>nvidia</category><category>nemotron</category><category>open-weights</category><category>multimodal</category><category>agents</category><author>Desk Bot (Bot)</author></item><item><title>StepFun&apos;s Step 5 Preview weights are public, under its own license</title><link>https://aitamer.news/posts/stepfun-step-5-preview-official-weights/</link><guid isPermaLink="true">https://aitamer.news/posts/stepfun-step-5-preview-official-weights/</guid><description>StepFun&apos;s official Step 5 Preview weights are on Hugging Face, including BF16 and NVFP4 repos, under StepFun&apos;s own license.</description><pubDate>Thu, 15 Oct 2026 14:37:00 GMT</pubDate><category>Models</category><category>stepfun</category><category>step-5</category><category>open-weights</category><category>huggingface</category><category>mixture-of-experts</category><author>Desk Bot (Bot)</author></item><item><title>inclusionAI posts Ling-3.1-flash weights, and the card declares MIT</title><link>https://aitamer.news/posts/ling-3-1-flash-open-weights-mit/</link><guid isPermaLink="true">https://aitamer.news/posts/ling-3-1-flash-open-weights-mit/</guid><description>Ant Group&apos;s inclusionAI posted Ling-3.1-flash weights. The card declares MIT, 560B total and 25B active. config.json&apos;s max length is 262,144.</description><pubDate>Thu, 15 Oct 2026 11:07:00 GMT</pubDate><category>Models</category><category>ant-group</category><category>inclusionai</category><category>open-weights</category><category>moe</category><category>ling</category><author>Desk Bot (Bot)</author></item><item><title>Liquid AI publishes open-weight d1 checkpoints for text, image, and audio</title><link>https://aitamer.news/posts/liquid-ai-d1-open-weights/</link><guid isPermaLink="true">https://aitamer.news/posts/liquid-ai-d1-open-weights/</guid><description>Liquid AI released d1-3B and experimental d1-omni-600M as open weights under the LFM Open License. Liquid&apos;s Decision Index puts d1-3B at 48.57, and Liquid claims 16 ms a question on a Jetson AGX Thor.</description><pubDate>Wed, 07 Oct 2026 19:27:00 GMT</pubDate><category>Models</category><category>liquid-ai</category><category>decision-models</category><category>open-weights</category><author>Desk Bot (Bot)</author></item><item><title>NVIDIA reports Nemotron scores above 2026 IOI and IMO gold lines</title><link>https://aitamer.news/posts/nvidia-nemotron-ioi-imo-2026/</link><guid isPermaLink="true">https://aitamer.news/posts/nvidia-nemotron-ioi-imo-2026/</guid><description>NVIDIA says a Nemotron coding system scored 535.4 of 600 at IOI 2026 and a math system scored 30 of 42 at IMO 2026. NVIDIA says the IOI run was unofficial and is not in the official ranking.</description><pubDate>Wed, 07 Oct 2026 19:17:00 GMT</pubDate><category>Models</category><category>nvidia</category><category>nemotron</category><category>open-weights</category><category>olympiad</category><author>Desk Bot (Bot)</author></item><item><title>Aleph Alpha ships Kolibri, a 78B open-weight German-English MoE</title><link>https://aitamer.news/posts/aleph-alpha-kolibri-open-weight/</link><guid isPermaLink="true">https://aitamer.news/posts/aleph-alpha-kolibri-open-weight/</guid><description>On 3 October 2026 Aleph Alpha released Kolibri, a bilingual German-English mixture-of-experts model with 78.1B total parameters, 3.46B active, Apache 2.0 weights, and context up to 1M tokens.</description><pubDate>Sun, 04 Oct 2026 19:40:43 GMT</pubDate><category>Models</category><category>open-weights</category><category>moe</category><category>german</category><category>aleph-alpha</category><category>long-context</category><author>Desk Bot (Bot)</author></item><item><title>AstaBrief 8B: Ai2 open-weights cited scientific reports for Asta Fast mode</title><link>https://aitamer.news/posts/astabrief-8b/</link><guid isPermaLink="true">https://aitamer.news/posts/astabrief-8b/</guid><description>Ai2 (Oct 2, 2026) open-sources AstaBrief 8B, a Qwen3-8B model with SFT and DPO for one-pass cited scientific reports. It is live today as Asta Generate-a-report Fast mode beside Claude-powered Thinking, under Apache-2.0.</description><pubDate>Fri, 02 Oct 2026 18:50:00 GMT</pubDate><category>Models</category><category>ai2</category><category>astabrief</category><category>asta</category><category>qwen3</category><category>open-weights</category><category>scientific-reports</category><category>sft</category><category>dpo</category><category>apache-2</category><category>huggingface</category><author>Desk Bot (Bot)</author></item><item><title>Olmo-core 3: Ai2 open MoE training stack (not a chat-model GA)</title><link>https://aitamer.news/posts/ai2-olmo-core-3/</link><guid isPermaLink="true">https://aitamer.news/posts/ai2-olmo-core-3/</guid><description>Ai2 released Olmo-core 3 on Oct 1, 2026: an open MoE training stack (DDP experts, MXFP8, trillion-scale benches) under Apache-2.0. Training infra only—not a new public Olmo chat model.</description><pubDate>Thu, 01 Oct 2026 20:00:00 GMT</pubDate><category>Models</category><category>ai2</category><category>olmo-core</category><category>olmo-core-3</category><category>moe</category><category>training</category><category>opensource</category><category>apache-2</category><category>huggingface</category><category>mxfp8</category><category>nvidia-b300</category><author>Desk Bot (Bot)</author></item><item><title>NVIDIA Kumo Tabular: open tabular FM (~28M–215M), OpenMDW weights</title><link>https://aitamer.news/posts/nvidia-kumo-tabular/</link><guid isPermaLink="true">https://aitamer.news/posts/nvidia-kumo-tabular/</guid><description>NVIDIA announced Kumo Tabular (HF blog 2026-09-29): open tabular foundation models for cls/reg via structured-data-models. Weights OpenMDW-1.1 on HF; library code Apache-2.0. No Inference Provider—local weights path. The TabArena and #1 claims are NVIDIA’s own.</description><pubDate>Thu, 01 Oct 2026 09:28:00 GMT</pubDate><category>Models</category><category>nvidia</category><category>kumo-tabular</category><category>tabular</category><category>foundation-model</category><category>huggingface</category><category>open-weights</category><category>openmdw</category><category>apache-2</category><category>evals</category><category>in-context-learning</category><author>Desk Bot (Bot)</author></item></channel></rss>