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AI 모델 2119
- Qwen3.8-27B-GGUF↗
batiai
> ### Qwen's newest 27B — on your Mac, in Korean, in one line. > **Weights dropped 2026‑08‑13.** Quantized the same week, straight from the official release. > Six sizes from **10 GB**, vision projector included, **Korean output actually tested** — and > Ollama tags published alongside, which most GGUF drops skip. Qwen's newest generation, built on the Qwen3.5 architecture — and the first 27B they call "deployment‑friendly" while keeping frontier behaviour. Straight from the official card: Architecture/context figures from the official model card. Qwen's published benchmark numbers are for the **full‑precision base model** — we don't restate them here, because what we can honestly report is what *these quants* did on our machine (below).
- finbert-esg-9-categories↗
yiyanghkust
ESG analysis can help investors determine a business' long-term sustainability and identify associated risks. **FinBERT-esg-9-categories** is a FinBERT model fine-tuned on about 14,000 manually annotated sentences from firms' ESG reports and annual reports. **finbert-esg-9-categories** classifies a text into nine fine-grained ESG topics: *Climate Change, Natural Capital, Pollution & Waste, Human Capital, Product Liability, Community Relations, Corporate Governance, Business Ethics & Values, and Non-ESG*. This model complements **finbert-esg** which classifies a text into four coarse-grained ESG themes (*E, S, G or None*). Detailed description of the nine fine-grained ESG topic definition, some examples for each topic, training sample, and the model’s performance can be found **here**.
- DFN5B-CLIP-ViT-H-14-378↗
apple
A CLIP (Contrastive Language-Image Pre-training) model trained on DFN-5B. Data Filtering Networks (DFNs) are small networks used to automatically filter large pools of uncurated data. This model was trained on 5B images that were filtered from a pool of 43B uncurated image-text pairs (12.8B image-text pairs from CommonPool-12.8B + 30B additional public image-text pairs). This model has been converted to PyTorch from the original JAX checkpoints from Axlearn (https://github.com/apple/axlearn). These weights are directly usable in OpenCLIP (image + text). - **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification. - **Dataset:** DFN-5b - **Papers:** - Data Filtering Networks: https://arxiv.org/abs/2309.17425 - **Samples Seen:** 39B (224 x 224) + 5B (384 x 384) | dataset | metric | |:-----------------------|---------:| | ImageNet 1k | 0.84218 | | Caltech-101 | 0.954479 | | CIFAR-10 | 0.9879 | | CIFAR-100 | 0.9041 | | CLEVR Counts | 0.362467 | | CLEVR Distance | 0.206067 | | Country211 | 0.37673 | | Describable Textures | 0.71383 | | EuroSAT | 0.608333 | | FGVC Aircraft | 0.719938 | | Food-101 | 0.963129 | | GTSRB | 0.679018 | | ImageNet Sketch | 0.73338 | | ImageNet v2 | 0.7837 | | ImageNet-A | 0.7992 | | ImageNet-O | 0.3785 | | ImageNet-R | 0.937633 | | KITTI Vehicle Distance | 0.38256 | | MNIST | 0.8372 | | ObjectNet 1 | 0.796867 | | Oxford Flowers-102 | 0.896834 | | Oxford-IIIT Pet | 0.966841 | | Pascal VOC 2007 | 0.826255 | | PatchCamelyon | 0.695953 | | Rendered SST2 | 0.566722 | | RESISC45 | 0.755079 | | Stanford Cars | 0.959955 | | STL-10 | 0.991125 | | SUN397 | 0.772799 | | SVHN | 0.671251 | | Flickr | 0.8808 | | MSCOCO | 0.636889 | | WinoGAViL | 0.571813 | | iWildCam | 0.224911 | | Camelyon17 | 0.711536 | | FMoW | 0.209024 | | Dollar Street | 0.71729 | | GeoDE | 0.935699 | | **Average** | **0.709421** |
- roberta-large-openai-detector↗
openai-community
- Model Details - Uses - Risks, Limitations and Biases - Training - Evaluation - Environmental Impact - Technical Specifications - Citation Information - Model Card Authors - How To Get Started With the Model **Model Description:** RoBERTa large OpenAI Detector is the GPT-2 output detector model, obtained by fine-tuning a RoBERTa large model with the outputs of the 1.5B-parameter GPT-2 model. The model can be used to predict if text was generated by a GPT-2 model. This model was released by OpenAI at the same time as OpenAI released the weights of the largest GPT-2 model, the 1.5B parameter version. - **Developed by:** OpenAI, see GitHub Repo and associated paper for full author list - **Model Type:** Fine-tuned transformer-based language model - **Language(s):** English - **License:** MIT - **Related Models:** RoBERTa large, GPT-XL (1.5B parameter version), GPT-Large (the 774M parameter version), GPT-Medium (the 355M parameter version) and GPT-2 (the 124M parameter version) - **Resources for more information:** - Research Paper (see, in particular, the section beginning on page 12 about Automated ML-based detection). - GitHub Repo - OpenAI Blog Post - Explore the detector model here
- sambanovasystems_-_SambaLingo-Arabic-Chat-70B-gguf↗
RichardErkhov
- Yi-1.5-9B-Chat-16K↗
01-ai
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. Model | Context Length | Pre-trained Tokens | :------------: | :------------: | :------------: | | Yi-1.5 | 4K, 16K, 32K | 3.6T
- Ingot-V-27B-Q2↗
dendriteholdings
A public mirror of a model that won on Ingot, a Bittensor subnet where miners quantize a fixed teacher model and the validator crowns the copy that stays closest to it. It is Prism ML's Ternary Bonsai 2 27B, refined: the same ternary codes, with the per-block scales and the norm weights re-tuned by the miner to bring it closer to Qwen3.8-27B. The weights are the miner's upload, byte for byte. Credit belongs to the miner, and to Prism ML for Bonsai 2.
- mms-tts-spa↗
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.
- lab1-cifar10↗
petitoff
Submission branch: `s_109197_103216`. Fine-tuned ImageNet ResNet-18 in Google Colab; only layer4 and fc are updated. The learning rate and epoch are selected by validation accuracy. The test split is used only after model selection. Seed: 42. Samples: train=10000, validation=2000, test=2000. Validation accuracy: 0.8870; test accuracy: 0.8795. Classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck. This model recognizes these ten classes and does not detect out-of-distribution images. CIFAR-10 consists of small 32x32 images, so predictions on arbitrary photographs may be unreliable.
- mms-tts-pap↗
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.