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AI Models 2119

  • opensearch-neural-sparse-encoding-doc-v2-mini↗

    opensearch-project

    The model should be selected considering search relevance, model inference and retrieval efficiency(FLOPS). We benchmark models' **zero-shot performance** on a subset of BEIR benchmark: TrecCovid,NFCorpus,NQ,HotpotQA,FiQA,ArguAna,Touche,DBPedia,SCIDOCS,FEVER,Climate FEVER,SciFact,Quora. Overall, the v2 series of models have better search relevance, efficiency and inference speed than the v1 series. The specific advantages and disadvantages may vary across different datasets. - **Paper**: Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers - **Fine-tuning sample**: opensearch-sparse-model-tuning-sample

  • GLM-OCR↗

    zai-org

    GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization. The model integrates the CogViT visual encoder pre-trained on large-scale image–text data, a lightweight cross-modal connector with efficient token downsampling, and a GLM-0.5B language decoder. Combined with a two-stage pipeline of layout analysis and parallel recognition based on PP-DocLayout-V3, GLM-OCR delivers robust and high-quality OCR performance across diverse document layouts. - **State-of-the-Art Performance**: Achieves a score of 94.62 on OmniDocBench V1.5, ranking #1 overall, and delivers state-of-the-art results across major document understanding benchmarks, including formula recognition, table recognition, and information extraction. - **Optimized for Real-World Scenarios**: Designed and optimized for practical business use cases, maintaining robust performance on complex tables, code-heavy documents, seals, and other challenging real-world layouts.

  • Nemotron-Labs-Diffusion-14B↗

    NVIDIA

    Nemotron-Labs-Diffusion is a tri-mode language model that supports both AR decoding and diffusion-based parallel decoding by simply switching the attention pattern of the same model during inference. The synergy between these two modes enables a third mode, called self-speculation: the same model performs diffusion-based parallel drafting and AR verification with shared KV cache, achieving high acceptance lengths and decoding efficiency. The seamless mode switching by simply changing attention patterns enables high efficiency at different concurrency levels in varying deployment scenarios with one single model. An optional LoRA adatper can be applied to the diffusion drafter in the linear self-speculation mode to further increase the acceptance length: NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the bias, explainability, safety & security, and privacy subcards.

  • Qwen3.8-27B-Uncensored-Aggressive-W4A16-AWQ↗

    philbert440

    4-bit weight (W4A16) AWQ quant of **Qwen3.8-27B-Uncensored-Aggressive** (α=1.15 recipe update), in `compressed-tensors` format. Vision tower and the grafted **MTP head** are preserved. Recipe update to the Aggressive line: the previous build ablated at α≈1.24, which a larger benchmark sweep showed **over-ablates** past the ~1.15 quality peak. This build uses **α=1.15** — more open and better on every measured axis. Quant: AWQ, group size 128, asymmetric, MSE observer, 256 Magpie-CoT calibration samples @1024 tokens.

  • sam-audio-small↗

    facebook

  • granite-speech-5.0-470m-turboctc↗

    ibm-granite

    > 🎉 **NEW (October 21, 2026): Granite Speech 5.0 TurboCTC is now multilingual!** > 🌍 **7 Languages** (En, Fr, De, Es, Pt, It, Ja) > ❓ **Native punctuation & capitalization** > 🚀 **Better English WER (5.51% vs. 5.78%)** > ⏪ **Previous English-only release: revision 947f59a** **Model Summary:** Granite Speech 5.0 TurboCTC is a compact 470 million parameter multilingual ASR model for English, French, German, Spanish, Portuguese, Italian and Japanese with very high inference speed that is well suited for deployment on laptops, smartphones and other edge devices. The model consists of a conformer acoustic encoder with block self-attention, self-conditioning and temporal downsampling with an output layer corresponding to 16,384 multilingual BPE units. It was trained on approximately 153,000 hours of multilingual audio from public corpora using Connectionist Temporal Classification (CTC) and inference is done non-autoregressively with greedy decoding. The model outputs punctuated and capitalized transcripts. We evaluated granite-speech-5.0-470m-turboctc on standard short-form English ASR benchmarks from the Open ASR leaderboard. Despite adding six languages and native punctuation, the multilingual update improves the average English WER over the original English-only release (5.51% vs. 5.78%):

  • Wald-26B-A4B↗

    org2ai

    An open decision model built on Gemma 4 26B-A4B, a mixture of experts with 26B parameters and about 4B active per token. You send a state and typed options, and it returns a probability for every option. It is served through a Jev-compatible `POST /v1/systemone` API on your own GPU. - **Probabilities, not text:** you get a probability for every option and have nothing to parse. - **Decision Index 0.3: 62.30**, complete public suite, one pass (our run with the official 0.3 kit). Jev 1.13 scores 57.96. Auto scores ≈ 65.9 (estimate from a 14 % sample; Auto exceeds the 1,000 ms admission latency, so one pass is the default). - **JevBench-XL TEST: 75.3 %** (Jev 1.13: 67.85 %). - **JevBench public set: 219 / 231** (macro 95.3 %). Jev 1.13 gets 200 / 231. - **Optional thinking:** an Auto mode can use Gemma 4's native thinking when the top probability is below 0.7; the default is one pass. - **Fast:** median **53 ms** in one pass, measured serially with one request in flight on a warm server with CUDA graphs. This is processing time on one RTX PRO 6000, over 200 Decision Index 0.3 requests. - **Open:** Apache-2.0, BF16 weights (52 GB), served on one 96 GB GPU. 62.30 is our run of the complete public suite (140,620 requests, all answered) with the official 0.3 kit, in one pass, the default setting.

  • qwen3-tts-12hz-1.7b-ru-stress-cf↗

    siriusfreak

    A fine-tune of Qwen/Qwen3-TTS-12Hz-1.7B-Base that puts Russian word stress where the text marks it — including marks that contradict the dictionary. The mark is the combining acute U+0301 after the stressed vowel: «за́мок» / «замо́к», «сто́ит» / «стои́т», «до́кументы». **Русский.** Дообучение Qwen3-TTS 1.7B: модель ставит ударение туда, где стоит знак U+0301 после ударной гласной, в том числе против словаря. Главное отличие от предыдущих адаптеров — контрафактный штраф при обучении и метки, сверенные со звуком. Ниже — результаты и как обучали. Judged from the audio by a stress detector (see *Evaluation*), 130 sentences, ~730 marks against the dictionary over two sampling seeds, 95% interval ±1.5 points. On ordinary text marked by RUAccent the model departs from the marks in 0.1% of words (voicy 0.6%), with CER 2.9% through Whisper (voicy 4.4%). Homographs (за́мок/замо́к, му́ки/муки́, …) are followed in 98–100% of cases by all fine-tunes, voicy's included.

  • sam-vit-tiny-random↗

    fxmarty

  • MiniMax-H3-Turbo-Lora-Diffusers↗

    InstantX

    Diffusers / PEFT conversion of `larryvrh/MiniMax-H3-Turbo-Lora`: a LoRA that lets MiniMax-H3 render joint **video + synchronized stereo audio** in about **4 sampling steps** instead of the usual ~20. > ⚠️ **Early prototype.** Same caveat as the upstream release: under-trained preview weights, not production quality. They already beat the base model at 4 steps (sharper detail, cleaner / better-synced audio), but treat this as a work-in-progress taste, not a finished product. Prefer the non-EMA `ckpt500` weights by default. Converted from the upstream Turbo LoRA (bf16, `W_eff = W + lora_B @ lora_A`, **alpha = rank** so scale is 1). QKV is split into `to_q` / `to_k` / `to_v`, and SwiGLU `fc1` halves are swapped to match Diffusers' `[value; gate]` layout.