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AI 模型 84760
- tribev2↗
facebook
**A Foundation Model of Vision, Audition, and Language for In-Silico Neuroscience** 📄 Paper ▶️ Demo | 🤗 Weights TRIBE v2 is a deep multimodal brain encoding model that predicts fMRI brain responses to naturalistic stimuli (video, audio, text). It combines state-of-the-art feature extractors — **LLaMA 3.2** (text), **V-JEPA2** (video), and **Wav2Vec-BERT** (audio) — into a unified Transformer architecture that maps multimodal representations onto the cortical surface.
- CLIP-ViT-B-16-DataComp.XL-s13B-b90K↗
flavour
1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation 7. How To Get Started With the Model A CLIP ViT-L/14 model trained with the DataComp-1B (https://github.com/mlfoundations/datacomp) using OpenCLIP (https://github.com/mlfoundations/open_clip). As per the original OpenAI CLIP model card, this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model.
- gemma-3-1b-it-GGUF↗
gaianet
- Qwen3Guard-Stream-8B↗
geoffmunn
- Qwen2.5-Coder-14B-Instruct-Q8_0-GGUF↗
ggml-org
- Voxtral-Small-24B-2507-gguf↗
handy-computer
GGUF conversions of mistralai/Voxtral-Small-24B-2507 for use with transcribe.cpp. Ported from upstream commit da5b424, pinned 2026-06-05. Validated against the Transformers reference at transcribe.cpp commit dac22fa on 2026-06-05. Offline audio-LLM speech-to-text and speech translation. A Whisper-large-v3 bidirectional audio encoder feeds a 4-frame-group projector (375 audio tokens per 30 s chunk) into a Mistral-Small-24B causal LM (40 layers, GQA 32/8, NEOX RoPE, SwiGLU) via audio-token injection. Takes a 16 kHz mono WAV and produces a transcript via greedy decoding. The larger sibling of Voxtral Mini 3B — same encoder, projector, frontend, and tokenizer, with a scaled-up decoder.
- bert-stock-sentiment-v1↗
hasnain43
This is a BERT-based model fine-tuned on a dataset of stock market news headlines to perform **sentiment analysis**. The labels are: The model is intended for **financial news and headlines**, especially useful for trading, sentiment scoring, or market analysis pipelines. tokenizer = AutoTokenizer.from_pretrained("hasnain43/bert-stock-sentiment-v1") model = AutoModelForSequenceClassification.from_pretrained("hasnain43/bert-stock-sentiment-v1") model.eval()
- opus-mt-tc-big-ko-en↗
Helsinki-NLP
- Model Details - Uses - Risks, Limitations and Biases - How to Get Started With the Model - Training - Evaluation - Citation Information - Acknowledgements Neural machine translation model for translating from Korean (ko) to English (en). This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of Marian NMT, an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from OPUS and training pipelines use the procedures of OPUS-MT-train. **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation (transformer-big) - **Release**: 2022-07-28 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): kor - Target Language(s): eng - **Original Model**: opusTCv20210807-sepvoc_transformer-big_2022-07-28.zip - **Resources for more information:** - OPUS-MT-train GitHub Repo - More information about released models for this language pair: OPUS-MT kor-eng README - More information about MarianNMT models in the transformers library - [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
- granite-4.2-30b-GGUF↗
ibm-granite
> [!NOTE] > This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model. > > Please reference the base model's full model card here: > https://huggingface.co/ibm-granite/granite-4.2-30b
- gemma-4-31B-it-SDFT-Heretic-RP↗
Ilya626