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AI 模型 2119
- vjepa2-vitg-fpc64-256↗
facebook
A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale. The code is released in this repository. V-JEPA 2 is intended to represent any video (and image) to perform video classification, retrieval, or as a video encoder for VLMs. To load a video, sample the number of frames according to the model. For this model, we use 64.
- TerraMind-1.0-tiny↗
ibm-esa-geospatial
TerraMind is the first multimodal any-to-any generative foundation model for Earth Observation jointly developed by IBM, ESA, and Forschungszentrum Jülich. TerraMind uses a dual-scale transformer-based encoder-decoder architecture, simultaneously processing pixel-level and token-level data. The model was pre-trained on 500B tokens from 9M spatiotemporally aligned multimodal samples from the TerraMesh dataset. Modality-specific patch embeddings allow direct processing of raw inputs, while modality-specific FSQ-VAEs are used for image tokenization. For sequence-like modalities such as coordinates, an adapted WordPiece tokenizer is employed. During pre-training, TerraMind leverages masked token reconstruction, learning complex cross-modal correlations to generate high-quality latent representations.
- roberta-base-go_emotions↗
SamLowe
该模型的 ONNX 格式版本（包括 INT8 量化 ONNX 版本）现已在 https://huggingface.co/SamLowe/roberta-base-go_emotions-onnx. 上提供。这些版本在推理时速度更快，尤其适用于较小的批量大小，大幅缩减了推理所需的依赖项体积，使模型的推理更具跨平台性；对于量化版本而言，在仅需推理的情况下，还能将模型文件/下载大小减少 75%，同时几乎保持全部精度。go_emotions 基于 Reddit 数据，包含 28 个标签。这是一个多标签数据集，对于任意给定的输入文本，可能适用一个或多个标签，因此该模型属于多标签分类模型，针对任意给定输入文本输出 28 个“概率”浮点数。通常会对每个标签的概率应用 0.5 的阈值来进行预测。该模型使用 `AutoModelForSequenceClassification.from_pretrained` 在 `problem_type="multi_label_classification"` 上进行了 3 个 epoch 的训练，学习率为 2e-5，权重衰减为 0.01。
- rut5-base-summ↗
d0rj
Finetuned ai-forever/ruT5-base for text and dialogue summarization. - d0rj/samsum-ru - IlyaGusev/gazeta - zjkarina/matreshka - rcp-meetings/rudialogsum_v2 - GEM/wiki_lingua - mlsum > Scheduler, optimizer and trainer states are saved into this repo, so you can use that to continue finetune with your own data with existing gradients.
- hubert-large-ll60k↗
facebook
The large model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. The model was pretrained on Libri-Light.
- whisper-large-v3-turbo↗
OpenAI
Whisper 是一款最先进的自动语音识别（ASR）和语音翻译模型，由 OpenAI 的 Alec Radford 等人在论文《Robust Speech Recognition via Large-Scale Weak Supervision》中提出。Whisper 在超过 500 万小时的标注数据上进行训练，在零样本设置下展现出强大的泛化能力，适用于众多数据集和领域。Whisper large-v3-turbo 是经过微调的剪枝版 Whisper large-v3。换言之，它与原模型完全相同，只是解码层的数量从 32 层减少到了 4 层。因此，该模型的速度大幅提升，但性能会略有下降。您可以在 这里 的 GitHub 讨论中找到更多详细信息。**免责声明**：本模型卡片的内容部分由 🤗 Hugging Face 团队撰写，部分则直接复制粘贴自原始模型卡片。
- gemma-3-12b-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 and 1B with 2 trillion tokens. 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.
- Qwen3.6-VL-REAP-26B-A3B-GGUF↗
keithnull
GGUF quantizations of atbender/Qwen3.6-VL-REAP-26B-A3B, a REAP-pruned variant of Qwen3.6-VL. **Both the language model (text quants) and the vision tower (mmproj) are included** - drop the mmproj alongside any text quant for full multimodal (image + text) inference. Each text quant was scored against the bf16 reference using `llama-perplexity` on the wikitext-2-raw test split (580 chunks, n_ctx=512, ~297k tokens). Bench run on a single RTX PRO 6000 (Blackwell, 96 GB). On Apple Silicon (llama.cpp, q8_0 KV cache): **Q4_K_M** had the best speed/quality trade-off across both standalone code-gen and agentic tasks. Q3_K_S held up reasonably well on quality at a smaller footprint, and IQ4_XS produced correct outputs but ran noticeably slower in the same harness. Your mileage may vary depending on your hardware and setup.
- Samantha-1.11-CodeLlama-34B-GGUF↗
TheBloke
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d36d5be95a0d9088b674dbb27354107221 They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
- samantha-mistral-7B-GGUF↗
TheBloke