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AI 模型 84757
- bert-base-cased↗
google-bert
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:
- Qwen3.8-27B-MLX-8bit↗
lmstudio-community
_👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: Qwen **Original model**: Qwen3.8-27B **MLX quantization**: provided by LM Studio team using mlx_vlm 🙏 Special thanks to the Apple Machine Learning Research team for creating MLX.
- indonesian-roberta-base-posp-tagger↗
w11wo
This model is a fine-tuned version of flax-community/indonesian-roberta-base on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.1395 - Precision: 0.9625 - Recall: 0.9625 - F1: 0.9625 - Accuracy: 0.9625 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - Transformers 4.37.2 - Pytorch 2.2.0+cu118 - Datasets 2.16.1 - Tokenizers 0.15.1
- ModernBERT-base↗
answerdotai
1. Model Summary 2. Usage 3. Evaluation 4. Limitations 5. Training 6. License 7. Citation ModernBERT is a modernized bidirectional encoder-only Transformer model (BERT-style) pre-trained on 2 trillion tokens of English and code data with a native context length of up to 8,192 tokens. ModernBERT leverages recent architectural improvements such as: - **Rotary Positional Embeddings (RoPE)** for long-context support. - **Local-Global Alternating Attention** for efficiency on long inputs. - **Unpadding and Flash Attention** for efficient inference.
- qwen2.5-3b-instruct↗
阿里巴巴 · Qwen
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains. - Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots. - **Long-context Support** up to 128K tokens and can generate up to 8K tokens. - **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. **This repo contains the instruction-tuned 3B Qwen2.5 model**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings - Number of Parameters: 3.09B - Number of Paramaters (Non-Embedding): 2.77B - Number of Layers: 36 - Number of Attention Heads (GQA): 16 for Q and 2 for KV - Context Length: Full 32,768 tokens and generation 8192 tokens
- whisper-large-v3↗
OpenAI
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 has the same architecture as the previous large and large-v2 models, except for the following minor differences: 1. The spectrogram input uses 128 Mel frequency bins instead of 80 2. A new language token for Cantonese
- Qwen3.5-0.8B↗
阿里巴巴 · Qwen
> [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. > > In light of its parameter scale, the intended use cases are prototyping, task-specific fine-tuning, and other research or development purposes. Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. - **Unified Vision-Language Foundation**: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
- Z Image Turbo↗
待核实厂商
Z Image Turbo API（APIMart）：阿里巴巴推出的超快速文生图模型，支持灵活设置多种宽高比，按张计费且单张图片价格低。开通会员，API 调用立省20%起。
- Wan 2.7 Image↗
待核实厂商
Wan 2.7 Image API（APIMart）：wan2.7-image 根据文字和最多 9 张参考图生成、编辑最高 2K 的图片，一次可出最多 12 张组图，按张计费。开通会员立省20%起。
- Wan 2.6↗
待核实厂商
Wan 2.6 API（APIMart）：调用阿里巴巴 Wan2.6，文生视频与图生视频，5、10 或 15 秒，720p/1080p，支持多镜头和音频。开通会员，API 调用立省20%起。