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AI 모델 2119

• mms-tts-ind↗

  facebook

  This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to synthesise speech with different rhythms from the same input text.

• StartLux-Decision-v1-35B-A3B↗

  startlux-models

  StartLux-Decision-v1-35B-A3B answers typed questions about a state: pick one of several options, yes or no, or a rating on a scale. The state can be text, JSON or images, up to 262,144 tokens (256K). Every question comes back with a probability for each option. Requests and responses use the TypeSafe `/v1/systemone` format, so clients written for Jev work unchanged. It is a mixture of experts: about 35B parameters, of which about 3B are active for each token. It answers in half the time of StartLux-Decision-v1-27B, has the highest JevBench and Intern-Decision numbers of the family, and is 2.3 points below the 27B on Decision Index 0.2.1. JevBench public counts the correct answers on the 231 public items in the Intern-Decision bundle; Intern avg is the average accuracy over its seven suites; DI is the Decision Index under both editions, the public board's values for the other systems. A blank cell means the number is not published. StartLux-Decision latencies are for one H200, with the three questions answered in one forward pass. ¹ Intern-Decision's own measurement on an RTX 4090. ² The server time the TypeSafe API gateway reports for the same request, mean of 100, network left out as in ours; Intern-Decision reports 109.7 ms end to end.

• TabSTAR-v1.1-cls-20261011↗

  alana89

  TabSTAR-v2 classification checkpoint, epoch 17 of `cls50k-active-261004_233909`. Pretrained checkpoint; this file records how it was trained and how it differs from the original TabSTAR (v1). - **Task type**: classification only (separate model per task type) - **Pool**: decontaminated T4 pool `t4_tabstarv1.1_runs_decontamination`, 200,142 classification tables; 50,000 sampled per epoch (`--num_datasets 50000`, `--pool_mode active`) - **Learning rate**: 1e-4, **weight decay**: 1e-3, linear warmup over the first 10% of steps - **Epochs**: 25 planned, early-stopping patience 5 - **Batch size**: 512 global (micro-batch 32, no gradient accumulation) - **Text encoder**: `intfloat/e5-small-v2`, top 6 layers unfrozen, text batch size 1024 - **Finetunability check**: every epoch, 30 minute timeout per check; epoch 17 is among the top finetunability Elo epochs Downstream finetuning (TabArena-Lite) uses the repo defaults, none tuned per checkpoint:

• Depth-Anything-V2-Base-hf↗

  depth-anything

  Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth estimation (MDE) model with the following features: - more fine-grained details than Depth Anything V1 - more robust than Depth Anything V1 and SD-based models (e.g., Marigold, Geowizard) - more efficient (10x faster) and more lightweight than SD-based models - impressive fine-tuned performance with our pre-trained models Depth Anything V2 was introduced in the paper of the same name by Lihe Yang et al. It uses the same architecture as the original Depth Anything release, but uses synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. The original Depth Anything model was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al., and was first released in this repository. Depth Anything V2 leverages the DPT architecture with a DINOv2 backbone.

• pythia-1b↗

  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.**

• d1-3B↗

  LiquidAI

  d1-3B is a 3B parameter **decision model** built on LFM2.5-VL-3B. You give it a state (text, JSON, images, or a mix) and a set of questions. It returns calibrated, typed answers in **one forward pass with zero output tokens**. - **Best decision model under 10B on the Decision Index 0.2.1**: 48.57, ahead of every 4B and 9B model and of Decider 35B-A3B (47.11). - **Multimodal**: images and text in the same state. It scores 74.1 on 11 public image benchmarks (LFM2.5-VL-3B: 73.9). - **Fast**: 8 ms a decision on an NVIDIA RTX 4090, 9 ms on an AMD MI325X, 30 ms on an Apple M5 Pro. Find more information about open d1 in our blog post.

• whisper-large-v3-turbo↗

  unsloth

  Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting. Whisper large-v3-turbo is a finetuned version of a pruned Whisper large-v3. In other words, it's the exact same model, except that the number of decoding layers have reduced from 32 to 4. As a result, the model is way faster, at the expense of a minor quality degradation. You can find more details about it in this GitHub discussion. **Disclaimer**: Content for this model card has partly been written by the 🤗 Hugging Face team, and partly copied and pasted from the original model card.

• Tessera-27B-B-CJK-GGUF↗

  ZelligeAI

  This repo holds GGUF files of Tessera-27B-B-CJK for llama.cpp. The main repo, ZelligeAI/Tessera-27B-B-CJK, holds the merged bf16 weights. Apart from the GGUF section, this card is a copy of the main card. Its results come from the LoRA on Tinker, which matches the merged bf16 weights by a logprob check. They are not measurements of the files in this repo. Tessera-27B-B-CJK is a coding-agent model built on Qwen3.8-27B. It thinks in compressed CJK, a shorthand that mixes English, Chinese words and symbols, and on held-out agent tasks it uses 0.50-0.64x the base model's thinking tokens per turn. Answers and tool calls stay in plain English and code. UL stands for Unsloth layout, and 1 GB is 10^9 bytes. To load the BF16 model, point llama.cpp at part 1, which should read part 2 from the same folder. We have not tried this.

• AMD-Llama-135m↗

  amd

  AMD-Llama-135m is a language model trained on AMD Instinct MI250 accelerators. Based on LLama2 model architecture, this model can be smoothly loaded as LlamaForCausalLM with huggingface transformers. Furthermore, we use the same tokenizer as LLama2, enabling it to be a draft model of speculative decoding for LLama2 and CodeLlama. AMD-Llama-135m and AMD-Llama-135m-code can be loaded and used via huggingface transformers, here is a simple example. We use SlimPajama and project gutenberg dataset to pretrain our 135m model, around 670B training tokens in total. SlimPajama is a deduplicated version of RedPajama and sources from Commoncrawl, C4, GitHub, Books, ArXiv, Wikpedia and StackExchange. We droped the Books data from SlimPajama due to license issues and used project gutenberg dataset instead.

• Qwopus3.6-27B-v1-preview-GGUF↗

  Jackrong

  As part of the Qwopus series, this release continues the same core direction established in earlier versions: stronger reasoning quality, a more stable answer structure, and less stylistic drift across long-form responses. Instead of introducing a complicated multi-stage design, this preview emphasizes a cleaner and more controlled supervised fine-tuning recipe, with the goal of producing outputs that feel more coherent, deliberate, and aligned across different tasks. > [!TIP] > This model was trained using Unsloth, and the full training workflow runs end-to-end successfully in practice. Many thanks to the Unsloth team for building and maintaining such a practical training stack for open-model fine-tuning. The current version is trained on a mixed reasoning dataset built primarily from `Kassadin88/Claude-Distillation-Dataset`, with additional samples drawn from: