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AI 모델 2119

• ThonburianTTS↗

  biodatlab

  **Thonburian TTS** is a **Thai Text-to-Speech (TTS)** engine built on top of the F5-TTS. It generates **natural and expressive Thai speech** by leveraging **Flow-Matching diffusion techniques** and can **mimic reference voices** from short audio samples. The system supports: The **models** are released under the Creative Commons Attribution Non-Commercial ShareAlike 4.0 License (CC BY-NC-SA 4.0). We would like to acknowledge NSTDA Supercomputer Center (ThaiSC) project \#pv824003 for providing computing resources for this work.

• bert_uncased_L-2_H-512_A-8↗

  Google

  This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking). We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.

• Samlagast-7B-laser-bf16-i1-GGUF↗

  mradermacher

• whisper-large-v3-turbo-greek-greece↗

  sam8000

• SilentCipher↗

  Sony

  The code is at SilentCipher Repository Code for SilentCipher, a method of embedding watermarking in any audio with state-of-the-art robustness. Currently this repository supports audio at 16kHz and 44.1kHz. Checkout our paper for more details. We have posted some examples from existing watermarking algorithms and how they compare to our watermarking method at EXAMPLES In this paper, we address artefacts introduces by Deep learning-based watermarking methods and introduce a way to remove the need for perceptual losses which leads to stable training allowing us to achieve SOTA in terms of both perceptual quality and robustness against distortion. Unlike previous methods which work on 16kHz sampling rate, we also showcase our results on 44.1kHz sampling rates opening the path for practical applications.

• led-base-16384↗

  allenai

  As described in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan, *led-base-16384* was initialized from *bart-base* since both models share the exact same architecture. To be able to process 16K tokens, *bart-base*'s position embedding matrix was simply copied 16 times.

• pythia-2.8b↗

  EleutherAI

  The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research (see paper). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. We also provide 154 intermediate checkpoints per model, hosted on Hugging Face as branches. The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models match or exceed the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Previously, we released an early version of the Pythia suite to the public. However, we decided to retrain the model suite to address a few hyperparameter discrepancies. This model card lists the changes ; see appendix B in the Pythia paper for further discussion. We found no difference in benchmark performance between the two Pythia versions. The old models are still available, but we suggest the retrained suite if you are just starting to use Pythia. **This is the current release.**

• gemma-3-270m-it-unsloth-bnb-4bit↗

  unsloth

  Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens, the 1B with 2 trillion tokens, and the 270M with 6 trillion tokens. The knowledge cutoff date for the training data was August 2024. Here are the key components: The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.

• GLM-5.3-Flash-Uncensored-VQ-GGUF↗

  Baekpica

  > [!WARNING] > **Experimental low-bit release.** The routed experts, which hold most of the > weights, are stored at 1.625 or 2.625 bits per weight depending on the layer > (about 2.1 on average), far below common 4-bit GGUF quants. You may see > failures the source model rarely shows: reasoning that loops or never > reaches an answer, text that breaks mid-character (shown as `�`) or switches > language mid-word, and weaker answers in general. This is exploratory > quantization research, not a drop-in replacement for the source model. > > **The files can change.** This line of conversions is still being researched > (as of 2026-10-21, weighted Lloyd fitting of the codebooks is being tested), > so any file here may be replaced by a new revision. Replacements so far: two > since the first release on 2026-10-17. The 8-bit residual stage that > protects sensitive routed layers was added to layers 3–16 and then widened > to 19 layers (2026-10-18). The earlier files remain at the tags `q4k-v3` and > `r13p8-l3-16`. To keep a fixed build, download a pinned commit: > `hf download Baekpica/GLM-5.3-Flash-Uncensored-VQ-GGUF --revision `. > > **Problems or odd output?** Feel free to post them in the > Community tab, > even as rough notes. The prompt, sampling settings and runtime commit help > most. > **Status, 2026-10-18:** the files are the 19-layer R13+8 revision: the > residual stage now covers routed layers 3–17, 22, 27, 41 and 44 (before: > 3–16). Loading needs a ds4-dfm-rs build with GVQ1 container v2 support > (`main` since PR #96, > merge `34c46d18`); the runtime binds the residual stage on any routed layer, > so no runtime change is needed. Speed and memory of this revision on DGX > Spark are not measured yet. Previous revisions are kept at the tags > `r13p8-l3-16` > (R13+8 on layers 3–16) and > `q4k-v3` > (all routed layers R13). Experimental custom GVQ1 / GGUF v3 conversion of orcarouter/GLM-5.3-Flash-Uncensored-FP8 (`150bd5e3a957db747db1224cc4f8891bcfa7a7f0`), the abliterated (refusal-removed) build of Z.AI GLM-5.3-Flash in the official block-FP8 layout, for DGX Spark (GB10) with ds4-dfm-rs. Standard llama.cpp/Ollama/Transformers loading is not claimed.

• granite-speech-4.1-2b↗

  ibm-granite

  **Model Summary:** Granite Speech 4.1 2B is a compact and efficient speech-language model, specifically designed for multilingual automatic speech recognition (ASR) and bidirectional automatic speech translation (AST) for English, French, German, Spanish, Portuguese and Japanese. The model was trained on 174,000 hours of audio from public corpora for ASR and AST as well as synthetic datasets tailored to support Japanese ASR, keyword-biased ASR and speech translation. Granite Speech 4.1 2B was trained by modality aligning an intermediate checkpoint of granite-4.0-1b-base to speech on publicly available open source corpora containing audio inputs and text targets. Compared to its predecessor granite-4.0-1b-speech, this model has the same parameter count (the new naming convention reflects actual instead of base LLM size) and provides additional capabilities and improvements: * Higher transcription accuracy for multilingual ASR due to a novel dual-head CTC encoder with both graphemic and BPE outputs and frame importance sampling to focus on informative parts of the audio * Punctuation and truecasing for ASR and AST in all languages (including German noun capitalization) with a simple prompt change * Better keyword list biasing capability for enhanced recognition of names, acronyms and technical jargon Two additional model variants explore different capabilities and inference optimization: * granite-speech-4.1-2b-plus adds speaker-attributed ASR and word-level time stamps * granite-speech-4.1-2b-nar introduces a novel non-autoregressive architecture for higher throughput