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AI 모델 84757
- 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。
- Wan 2.7 Image Pro↗
待核实厂商
Wan 2.7 Image Pro API（APIMart）：wan2.7-image-pro 支持最高 4K 文生图与思考模式，并可用最多 9 张参考图编辑或生成最高 2K 的组图。开通会员立省20%起。
- Wan 2.5↗
待核实厂商
Wan 2.5 API（APIMart）：调用阿里巴巴 Wan2.5 Preview，文生视频与图生视频，5 或 10 秒，480p–1080p，每段视频都带声音。开通会员，API 调用立省20%起。
- Wan 2.6 I2V Flash↗
待核实厂商
Wan 2.6 I2V Flash API（Wan 2.6 Flash，APIMart）：wan2.6-i2v-flash 把一张图生成 2–15 秒的 720p/1080p 视频，可带音频或输出无声视频，按秒计费。
- Wan 2.6 I2V↗
待核实厂商
Wan 2.6 I2V API（APIMart）：wan2.6-i2v 把一张首帧图生成 5、10 或 15 秒的 720p/1080p 视频，支持多镜头与自定义音频。开通会员，API 调用立省20%起。