#Open source/Repo

AIShanghai AI Lab has open-sourced Intern-Decision, a family of 0.8B, 2B and 4B parameter models that return structured decisions with probabilities instead of free text. The developers self-report that the 4B model averages 90.02% accuracy across seven test suites, ahead of a commercial reference model at 88.74%, with about 44 milliseconds of local latency on a single RTX 4090. Weights are on Hugging Face, and MetaX says the models run on its hardware from launch.

AIXiaomi MiMo has released MiMo-V2.5-ASR, an open-source speech recognition model that the company says achieves state-of-the-art results across multiple benchmarks. The model supports bilingual Chinese–English recognition, Chinese dialects such as Wu, Cantonese, Hokkien, and Sichuanese, code-switching, and lyrics transcription. It is also designed to handle noisy environments and multi-speaker conversations.

AIXiaomi introduces the MiMo-V2.6 series of models, as stated on its official MiMo page. The source excerpt gives only the tagline, describing frontier intelligence across all modalities, so specific capabilities, benchmarks, and availability cannot be confirmed from it.

AIJetBrains has released Mellum2.1, a 12B mixture-of-experts thinking model with 2.5B active parameters, under Apache 2.0 on Hugging Face. Post-training reinforcement learning in real software repositories raised SWE-bench Verified from 2.0 to 47.0, according to JetBrains' self-reported results. Qwen3.5-9B still leads on SWE-bench Pro, GPQA Diamond and AIME, and GGUF builds start at 7.0 GB for local use.

AICarbon-A is an open model that predicts gene locations directly from DNA, and it has been used to annotate genomes from over 22,000 species. The release includes a database of 566 million gene candidates, about 16 times the gene annotations in the RefSeq dataset. Wet-lab RNA experiments supported 239 candidates missing from RefSeq across cats, Syrian hamsters, chickens, and Arabidopsis.

Why it matters: The source ties an open gene-annotation model to specific wet-lab checks and gene counts, helping readers judge how far its predictions extend beyond well-studied genomes.

AIThe team released Carbon-A, an open model that finds genes directly in DNA, along with a database of 566.34 million candidate genes across 22,617 species. The model reads genomes without needing a close relative, and wet-lab validation in cats, chickens, and arabidopsis is cited, with 239 genes found missing from reference annotations of common species. The authors say the model marks gene locations but does not design DNA or predict gene function.

AIJetBrains released Mellum2.1, a 12B mixture-of-experts model with 2.5B active parameters under the Apache 2.0 license, built for coding agents. Post-training shifted to reinforcement learning across thousands of environments and millions of sandboxed runs, and the model is available on Hugging Face. The source reports gains over Mellum2 on LiveCodeBench, AIME, GPQA Diamond, BFCL v4, IFEval, and SWE-bench Verified, and says it serves almost twice the tokens of Qwen3.5-9B under heavy load.

Why it matters: The post shows how reinforcement learning in real sandboxed environments changed a compact open model's repository work, with benchmark gains against Mellum2 and two peers.

AIPerplexity has released pplx-embed-v2-late, a pair of ColBERT-style multimodal embedding models in 0.6B and 9B sizes that retrieve text, images and rendered PDF pages in a shared embedding space. Both are available on Hugging Face under the MIT license, while a hosted API endpoint is planned but not yet live.

AIGoogle released EmbeddingGemma 2 on Hugging Face, and Paige Bailey called it a step toward open models for open science. The post links the model and cites earlier EmbeddingGemma-based projects, including medical, geographic, oncology, and PubMed embedding models.

AILiquid AI has released its d1 decision models, with d1-3B recommended for high-quality text and vision tasks and d1-omni-600M offered as an experimental option for smaller footprints. The weights are available for download, fine-tuning, and local deployment.

AILiquid AI released Open d1, two open-weight multimodal models in its d1 decision model family. The d1-3B model supports text and vision, while d1-omni-600M supports text plus image or text plus audio. The source says the models are meant for real-time decision making across data centers, RTX workstations, and Jetson edge devices.

AILiquid AI released two open-weight decision models, d1-3B and d1-omni-600M (experimental), built on its Liquid Foundation Models and available on Hugging Face. d1-3B scores 48.57 on the Decision Index 0.2.1, the highest among decision models under 10B parameters, and answers a question in 16 ms on an NVIDIA Jetson AGX Thor and under 50 ms on a Jetson Orin Nano. The models support text and images (d1-3B) or text with image or audio (d1-omni-600M).

AIPerplexity is open-sourcing pplx-embed-v2-late, multi-vector embedding models for text and images in one shared space, in 9B and 0.6B sizes. The 9B model can index multimodal data, the 0.6B model can run queries on device, and PDF pages can be searched without OCR. The author reports 92.4% on MADQA and 64% on BrowseComp+, with weights available on Hugging Face.

AIPerplexity has published both pplx-embed-v2 models on Hugging Face, with the collection available at the linked URL. The post provides no further details on model sizes, benchmarks, or capabilities.

AIPerplexity has released pplx-embed-v2-late, two late-interaction embedding models that retrieve text, images, and pages within a shared embedding space for cross-model querying. The company says both models achieve frontier performance and are publicly available on Hugging Face.

AISmallest AI's Pulse ranks first on the Diarization + ASR track of Voice Arena's Diarization Bench with a 24.4% DER, where lower is better. The next system in that track scores 40.7% DER, and Pulse misses 4.4% of reference speech, the lowest in the track.

AIMistral has released Mistral Large 4, nicknamed Le Chonk, a 1 trillion-parameter model it says can be used and customized by anyone. It is in preview, with a final version due by the end of the month, and is optimized for coding and cyberdefense as well as manufacturing, finance, and electrical engineering tasks. Mistral claims it is the most capable open-weight model developed outside China and says it was trained from scratch rather than through distillation.

AIvLLM Semantic Router and KR Labs released Vela 2.0, which makes span-level decisions covering safety checks, domain classification, PII spans, and unsupported claims in a single call. It comes in four sizes from 0.3B to 9B parameters under the Apache-2.0 license.

AIGoogle DeepMind released EmbeddingGemma 2, an open 740M-parameter embedding model that maps text, code, images, video, and audio into one 768-dimensional space. Text-only use needs a 270M-parameter footprint, about 191MB active RAM when quantized on a Pixel 11 Pro, while loading all modalities takes about 567MB. The reported MTEB Code NDCG@10 score is 78.68, about 14% above the first generation, and MTEB Multilingual v2 is 61.36, roughly flat.

AILiquid AI released two open-weight d1 decision models, d1-3B and d1-omni-600M, on Hugging Face. d1-3B scores 48.57 on the Decision Index v0.2.1 public split and answers a single question in 8 ms on an NVIDIA GeForce RTX 4090 and 50 ms on a Jetson Orin Nano. d1-omni-600M is an experimental checkpoint that handles text with images or audio and scores 15.95 on the same index.

Why it matters: The release pairs open-weight decision models with measured latency across Apple, NVIDIA, and Jetson hardware, showing how edge deployment changes what is practical.

AIGoogle DeepMind has released EmbeddingGemma 2, an open 740 million parameter model that maps text, images, audio, and video into one embedding space. It is built on the Gemma 4 architecture under an Apache 2.0 license and supports an 8K token context window. The company reports a code benchmark gain from 68.76 to 78.68 on MTEB Code and says the model can run on-device with about 567MB of active RAM for the full multimodal version on a Google Pixel 11 Pro.

Why it matters: The release shows how a 740M-parameter embedding model can cover text, code, images, audio, and video on local hardware, with memory and storage figures to compare against other on-device options.

AIGoogle releases EmbeddingGemma 2, its first native multimodal embedding model, built on Gemma 4 under Apache 2.0. It embeds over 100 languages, code, images, audio, and video into one vector, with an 8,192-token context and four sizes from 270M to 740M parameters. Matryoshka output dimensions of 768, 512, 256, or 128 are supported, and the model is available in Sentence Transformers and LiteRT-LM, with a reported 14% gain on MTEB Code.

Why it matters: The release extends an embedding model to text, code, images, audio, and video in one vector, a useful option for retrieval systems that mix media types.

AIPerplexity says its open-weights multimodal decision model pplx-decider-v1.1-27b ranks first on the new Hugging Face Decision Index 0.3 benchmark. The updated model is available to all, and it costs $0.02 per million input tokens, half the price of v1.

AIGoogle's EmbeddingGemma 2 is an open embeddings model for on-device use that covers code, image, video, audio, and text. It comes in modular sizes from 270M text/code to 740M full multimodal, supports Matryoshka truncation down to 128 dimensions, and reports a 14% gain on MTEB Code over v1 under an Apache 2.0 license. The author's post highlights the release and a Hugging Face demo, while the benchmark table compares it with several models.

AI跟着东锡老师学关键词： Sovereign AI

AIGoogle released EmbeddingGemma 2, a 740M-parameter open embedding model under Apache 2.0 that combines a 270M text model with vision (170M) and audio (300M) encoders. The 270M text model can run locally with 0.5GB of RAM, and the full multimodal model with 1GB, and Unsloth provides GGUF files and fine-tuning support.

AIGoogle has released the weights for EmbeddingGemma 2 on Hugging Face and Kaggle. The release includes out-of-the-box support for LiteRT, MediaPipe, LangChain, and LlamaIndex.

AIGoogle DeepMind has released EmbeddingGemma 2, a 740-million-parameter embedding model that maps text, images, audio, and video into a shared space and runs on local hardware under an Apache 2.0 license. Matryoshka Representation Learning lets developers truncate output vectors from 768 dimensions to 512, 256, or 128, and the model supports an 8K-token context window. The model weights are available on Hugging Face and Kaggle, with Gemini Enterprise Agent Platform availability coming soon.

Why it matters: The release shows how a 740M-parameter multimodal embedder runs locally with a 768-to-128 dimension truncation option, useful for judging on-device retrieval designs.

AIMistral announced Mistral Large 4, which it describes as a natively multimodal model with 1T parameters and 49B active. Mistral says it is available via API now, with open weights to follow at the end of October, and a Hugging Face page is listed for the release.

Why it matters: The quoted Mistral announcement gives specific size, activation, and API details, and the open-weights timing matters for teams weighing open model options.

AIClément Delangue of Hugging Face shared a link to a Hugging Face model page for mistralai/Mistral-Large-4.0-1T05-A52B. The post itself gives no further details about the model's capabilities, release terms, or benchmarks.

AIReflection announced Beam, a text-only 501B-total, 23B-active MoE model for coding, agentic, and scientific work, trained from scratch with full weights under Apache 2.0 promised this month. Self-reported results include 80.9 on SWE-bench Verified and 3–4x the inference efficiency of GLM 5.2, while the roundup notes that GLM 5.3, Kimi K3, Qwen 3.8 Max, and DeepSeek V4.1 Flash are generally ahead.

AIReflection AI, an Nvidia-backed startup, released Beam, its first open-weight large model, aimed at coding and agent tasks. The company says Beam is comparable to Z.ai's GLM-5.2 and is approaching Qwen3.8-Max on coding and agent work. Beam has 501 billion total parameters, with 23 billion activated per task in a sparse architecture.

AIReflection AI previewed Beam, an MoE open model with 501B total and 23B active parameters, claiming 3–4x better inference efficiency than GLM 5.2. The model was pretrained from scratch on 23.8T tokens in four weeks, and its RL run used 10,500 GB300 GPUs over four weeks, which the post describes as possibly the largest publicly recorded. Reflection positions Beam as a workhorse open model for enterprises, governments, and developers, with full weights due this month.

AIReflection AI introduced Beam, an agentic open model with 501B total parameters and 23B active parameters, trained end-to-end from scratch. The quoted announcement says it targets frontier reasoning efficiency and coding and agentic tasks, with full weights due this month. Clément Delangue, Hugging Face's CEO, reposted it with a welcome to the Reflection organization on Hugging Face.

Why it matters: The quoted announcement names Beam's parameter scale, active-parameter count, and coding and agentic focus, which helps readers gauge where it fits among open models.

AIOllama says it is excited about a new open model from Reflection AI and that more US models are coming to Ollama. Reflection AI's Beam is described as an agentic open model with 501B total parameters and 23B active, with full weights due this month.

AIIndexTeam released Index-Echo-S2ST-9B-FP4, an NVFP4 (W4A4) quantization of the Index-Echo-S2ST-9B speech-to-speech translation model, with only its text LLM backbone quantized. Perplexity rose from 3.8218 to 3.9650 (+3.75%) on a fixed corpus, while zh→en and en→zh outputs were semantically equivalent, and full FP4 speedup requires an NVIDIA Blackwell GPU.

AIIndexTeam released Index-Echo-S2ST-2B-FP4, an NVFP4 (W4A4) quantized version of the Index-Echo-S2ST-2B speech-to-speech translation model, with only the text LLM backbone quantized and the audio components kept in BF16. On a fixed corpus, perplexity rose from 5.9332 to 6.4980 (+9.52%), while zh->en and en->zh generations matched the original. Full FP4 acceleration requires an NVIDIA Blackwell GPU, and the model loads via compressed-tensors in vLLM or transformers.

AIIndexTeam published an NVFP4 (W4A4) quantized version of its Index-Echo-S2TT-9B speech-to-text translation model, quantizing only the text LLM backbone while keeping the audio tower and other components in BF16. On an NVIDIA A100, perplexity rose from 3.4155 to 3.5113 (+2.81%), with zh->en and en->zh outputs semantically equivalent under greedy decoding. Full FP4 speedup requires an NVIDIA Blackwell GPU, while older GPUs get only memory reduction.

AIIndexTeam has published an official NVFP4 (W4A4) quantized version of its Index-Echo-S2TT-2B speech-to-text translation model on Hugging Face. Only the text LLM backbone is quantized, while the audio tower, connector, and speech-synthesis components remain in BF16. Perplexity rises 5.80%, from 4.8772 to 5.1599, on a fixed corpus, and full FP4 speedup requires an NVIDIA Blackwell GPU.

AIIndexTeam released Index-Nailong-9B-FP4, an official NVFP4 (W4A4) quantization of the Index-Nailong-9B multilingual translation model, which covers 150 languages. In a validation on an NVIDIA A100 against the BF16 checkpoint, perplexity rose 3.10% (2.4339 to 2.5094), and zh-en and en-zh outputs were semantically equivalent. Full FP4 compute acceleration requires an NVIDIA Blackwell GPU, while older GPUs get memory savings only; the FP8 build is recommended for Hopper and Ampere.