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AI Models 2119

  • evf-sam↗

    YxZhang

  • moonshine-streaming-medium-gguf↗

    handy-computer

    Ported from upstream commit 57b8436, pinned 2026-05-06. Validated against the HF Transformers v5.7.0 reference at transcribe.cpp commit 0d312ce on 2026-05-06. English speech-to-text in both one-shot and streaming modes. An encoder-decoder ASR model designed for streaming use (ergodic encoder + sliding-window attention, 50 Hz time-domain frontend). Same family as moonshine-streaming-tiny and moonshine-streaming-small; deepest of the three (14 / 14 layers) and widest hidden dims (encoder 768 / decoder 640). Takes a 16 kHz mono WAV and produces a transcript. No translation, no multilingual capability, no timestamps. WER on the full LibriSpeech test-clean split (2,620 utterances). Figures without a commit were published before provenance was recorded.

  • SambaLingo-Japanese-Base-i1-GGUF↗

    mradermacher

  • legal-bert-small-uncased↗

    nlpaueb

    LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-train the different variations of LEGAL-BERT, we collected 12 GB of diverse English legal text from several fields (e.g., legislation, court cases, contracts) scraped from publicly available resources. Sub-domain variants (CONTRACTS-, EURLEX-, ECHR-) and/or general LEGAL-BERT perform better than using BERT out of the box for domain-specific tasks. This is the light-weight version of BERT-BASE (33% the size of BERT-BASE) pre-trained from scratch on legal data, which achieves comparable performance to larger models, while being much more efficient (approximately 4 times faster) with a smaller environmental footprint. I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras and I. Androutsopoulos. "LEGAL-BERT: The Muppets straight out of Law School". In Findings of Empirical Methods in Natural Language Processing (EMNLP 2020) (Short Papers), to be held online, 2020. (https://aclanthology.org/2020.findings-emnlp.261) \* LEGAL-BERT-BASE is the model referred to as LEGAL-BERT-SC in Chalkidis et al. (2020); a model trained from scratch in the legal corpora mentioned below using a newly created vocabulary by a sentence-piece tokenizer trained on the very same corpora.

  • GLM-5.3-NVFP4↗

    NVIDIA

    The NVIDIA GLM-5.3 NVFP4 model is the quantized version of ZAI's GLM-5.3 model, which is an auto-regressive language model that uses an optimized transformer architecture. GLM-5.3 is a Mixture-of-Experts (MoE) model for reasoning, coding, and agentic tasks; it uses the same base model as GLM-5.2, with gains from post-training, and uses sparse attention (with an IndexShare indexer) to support a long context. For more information, please check here. The NVIDIA GLM-5.3 NVFP4 model is quantized with Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA (GLM-5.3) Model Card from ZAI. **License type:** NVIDIA Proprietary **Governing Download Terms:** Use of this model is governed by NVIDIA Open Model Agreement. **Additional Information:** GLM-5.3 License. Third-Party Notice Text File: The original GLM-5.3 model are licensed under the GLM-5.3 License, included in this repository. The NVIDIA Open Model Agreement does not modify or supersede the GLM-5.3 License or its conditions.

  • P-Q-Image-2.1↗

    PrunaAI

    6 steps (v0.2), 5 or 8 steps (v0.1)  ·  Up to 6.3x faster  ·  No CFG  ·  Text-to-image and image editing **P-Q-Image-2.1** is a set of LoRA adapters that let `Qwen/Qwen-Image-2.1` generate and edit images in only 6 steps (v0.2), or 5 or 8 steps (v0.1). The adapters load on top of the base model, so the pipeline, text encoder, and VAE stay unchanged. > **v0.2: two new 6-step adapters.** > > - An advanced distillation for more faithful generation and editing. > - The first distillation removing need for prompt upsampling into the LoRA, thus significantly reducing end-to-end generation. > The v0.1 adapters remain available.

  • gemma-4-12b-it↗

    unsloth

    Gemma 4 12B can now be run and fine-tuned in Unsloth Studio . Read our guide . See all versions of Gemma 4 (GGUF, 16-bit etc.) in our collection . Example of Gemma 4 E4B (4-bit GGUF) running in Unsloth Studio with tool-calling: > [!Note] > This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.

  • svara-tts-v1↗

    kenpath

    **svara-TTS** is a developer-first multilingual TTS model for **19 languages** (18 Indic + Indian English). Built on an Orpheus-style discrete audio token approach, it targets **clarity, expressiveness, and low-latency** on commodity GPUs/CPUs. It supports light-weight **emotion/style control** (e.g., ` `, ` `, ` `, ` `) and simple **speaker identities** (`Language (Gender)`), with **zero-shot** adaptation paths. - **Languages (19):** Hindi, Bengali, Marathi, Telugu, Kannada, Bhojpuri, Magahi, Chhattisgarhi, Maithili, Assamese, Bodo, Dogri, Gujarati, Malayalam, Punjabi, Tamil, Nepali, Sanskrit, Indian English. - **Expressivity:** End-of-utterance style tags; natural prosody; code-switch aware. - **Latency & Deployment:** Works well with **GGUF** exports; suitable for edge/CPU scenarios. - **Adaptability:** **LoRA-friendly** for quick speaker/domain specialization. Try it live on the **Demo Space**, or on **Colab** Deployment scripts and **inference repo** will be available soon. Watch our Github for updates

  • phi-2↗

    Microsoft

    Phi-2 is a Transformer with **2.7 billion** parameters. It was trained using the same data sources as Phi-1.5, augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assessed against benchmarks testing common sense, language understanding, and logical reasoning, Phi-2 showcased a nearly state-of-the-art performance among models with less than 13 billion parameters. Our model hasn't been fine-tuned through reinforcement learning from human feedback. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more. Phi-2 has been integrated in the `transformers` version 4.37.0, please ensure that you are using a version equal or higher than it.

  • Samantha-v5-wizardlm2-GGUF↗

    mradermacher