# Pre-trained model

Aria-Base-64K
Aria Base 64K
Aria-Base-64K is one of the foundational models in the Aria series, designed for research purposes and further training. It emerged after the long text pre-training phase, trained on 33 billion tokens (21 billion multimodal and 12 billion language tokens, with 69% being long texts). It is suitable for further pre-training or fine-tuning on long video question answering datasets or long document question answering datasets, even in resource-constrained environments, through post-training with short instruction tuning datasets tailored for long text scenarios. The model can comprehend up to 250 high-resolution images or up to 500 medium-resolution images, maintaining robust foundational performance in both language and multimodal contexts.
AI Model
45.3K
Fresh Picks
GLM-4-9B-Chat
GLM 4 9B Chat
GLM-4-9B-Chat is the open-source version of the latest pre-trained model GLM-4 series released by Zhi谱 AI. It boasts advanced features such as multi-turn dialogue, web browsing, code execution, custom tool invocation, and long-text reasoning. It supports 26 languages including Japanese, Korean, and German, and has launched a model with support for a 1M context length.
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CogVLM2
Cogvlm2
CogVLM2, developed by a team from Tsinghua University, is a second-generation multimodal pre-trained dialogue model. It has achieved significant improvements in multiple benchmark tests, supports 8K content length and a resolution of 1344*1344 for images. CogVLM2 offers both Chinese and English versions that are open-source, achieving performance comparable to some non-open-source models.
AI Model
65.4K
Qwen1.5-32B
Qwen1.5 32B
Qwen1.5 is a decoder language model series based on the Transformer architecture, including models of various sizes. It features SwiGLU activation, attention QKV bias, and group query attention. It supports multiple natural languages and code. Fine-tuning is recommended, such as SFT, RLHF, etc. Pricing is free.
AI Model
55.8K
SpacTor-T5
Spactor T5
SpacTor is a new training procedure that includes (1) a mixed objective combining span corruption (SC) and replacement tag detection (RTD), and (2) a two-stage curriculum that optimizes the mixed objective in the initial \tau iterations and then transitions to standard SC loss. Experiments on various NLP tasks, using the encoder-decoder architecture (T5), show that SpacTor-T5 achieves comparable downstream performance to standard SC pre-training while reducing the pre-training iterations by 50% and the total FLOPs by 40%. Additionally, under the same computational budget, we find that SpacTor can significantly improve downstream benchmark performance.
AI Model
44.4K
Chinese Picks
Yi
Yi
Yi is a globally leading pre-trained model that has achieved SOTA international best performance indicators in multiple evaluations. It has a lightweight model size, surpassing large-scale open-source models, making it more friendly to the developer community. Suitable for individuals and research purposes, Yi boasts the ability to emerge as a large model, catering to diverse scenarios and satisfying the rigid needs of the open-source community. Yi's open-source model is completely open to academic research and simultaneously offers free commercial use application.
AI Model
98.0K
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Tencent Hunyuan Image 2.0
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OpenMemory MCP
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FastVLM
Fastvlm
FastVLM is an efficient visual encoding model designed specifically for visual language models. It uses the innovative FastViTHD hybrid visual encoder to reduce the time required for encoding high-resolution images and the number of output tokens, resulting in excellent performance in both speed and accuracy. FastVLM is primarily positioned to provide developers with powerful visual language processing capabilities, applicable to various scenarios, particularly performing excellently on mobile devices that require rapid response.
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42.5K
Chinese Picks
LiblibAI
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