INTELLECT-3 is a Mixture of Experts (MoE) inference model developed by Prime Intellect with a parameter scale of 106B (12B active parameters). It is supervised and fine-tuned based on GLM-4.5-Air-Base and trained through large-scale reinforcement learning, showing excellent performance in mathematics, coding, and inference tasks.

Natural Language ProcessingGguf

This is the GGUF quantized version of ArliAI's GLM-4.5-Air-Derestricted model. It uses llama.cpp and imatrix technology for optimized quantization processing, offering multiple quantization levels to suit different hardware configurations and performance requirements.

Natural Language ProcessingGguf

GLM-4.6 AWQ - INT4 is a 4-bit quantized version of the GLM-4.6 model, using the AWQ quantization method. It significantly reduces the storage and computing resource requirements while maintaining the model's performance. This model supports a 200K context length and shows significant improvements in coding, reasoning, and agent tasks compared to GLM-4.5.

Natural Language ProcessingTransformersMultiple Languages

GLM-4.6 is a new-generation text generation model launched by Zhipu AI. Compared with GLM-4.5, it has significant improvements in multiple aspects, including a longer context window, better coding performance, and stronger reasoning ability. This model performs excellently in multiple public benchmark tests and has a competitive advantage compared with leading models at home and abroad.

Natural Language ProcessingTransformersMultiple Languages

GLM-4.6 is a new-generation large language model developed by Zhipu AI. Compared with GLM-4.5, it has significant improvements in context processing, coding ability, and reasoning performance. This model supports a context length of 200K and performs excellently in multiple public benchmark tests, especially having competitive advantages in code generation, reasoning, and agent tasks.

Natural Language ProcessingTransformersMultiple Languages

GLM-4.6 is a new-generation text generation model launched by Zhipu AI. Compared with GLM-4.5, it has achieved significant improvements in context processing, encoding performance, reasoning ability, etc. It supports a context length of 200K and has stronger agent capabilities and refined writing abilities.

Natural Language ProcessingTransformersMultiple Languages

This is the Llama.cpp mixed layer quantization version of GLM-4.5-Air by the zai team. By adopting different quantization levels for each layer, a good balance is achieved between inference performance and model file size. Multiple quantization schemes are provided, suitable for running on machines with 64GB RAM.

Natural Language ProcessingGgufOther

This is a quantized version of the zai-org GLM-4.5 model, implemented through the quantization technology of llama.cpp, which can run efficiently under different hardware conditions. It offers a variety of quantization levels from Q8_0 to IQ1_S and supports text generation tasks. The model file size ranges from 77GB to 380GB.

Natural Language ProcessingGguf

GLM-4.1V-9B-Thinking is an open-source model developed based on the Vision-Language Model (VLM), aiming to explore the upper limit of the reasoning ability of vision-language models. By introducing the 'thinking paradigm' and reinforcement learning, it significantly improves the model's ability and reaches the leading level among VLMs with 10B parameters.

MultimodalTransformersMultiple Languages

GLM-4.1V-9B-Thinking is an open-source vision-language model based on the GLM-4-9B-0414 base model, specifically designed to explore the upper limit of the reasoning ability of vision-language models. It introduces the 'thinking paradigm' and uses reinforcement learning to significantly enhance its capabilities, performing excellently among VLMs with 10B parameters and rivaling or even surpassing models with larger parameters in multiple benchmark tasks.

MultimodalTransformersMultiple Languages

GLM-4.1V-9B-Thinking is an open-source vision-language model based on the GLM-4-9B-0414 foundation model. By introducing the 'thinking paradigm' and reinforcement learning, the model's performance has been significantly improved, reaching a leading level among VLMs with 10 billion parameters. It has even surpassed the 72 billion parameter Qwen-2.5-VL-72B in 18 benchmark tasks.

MultimodalTransformersMultiple Languages

GLM-4.1V-9B-Thinking is an open-source vision-language model based on the GLM-4-9B-0414 foundation model, focusing on improving the reasoning ability in complex tasks and supporting a 64k context length and 4K image resolution.

MultimodalTransformersMultiple Languages

GLM-4.1V-9B-Base is an open-source vision-language foundation model developed by Zhipu AI. It has 9 billion parameters, focuses on multimodal reasoning capabilities, supports both Chinese and English, and can process images with up to 4K resolution and a context length of 64K.

MultimodalTransformersMultiple Languages

GLM-Z1-9B-0414 is a bilingual text generation model supporting both Chinese and English, utilizing the GGUF format and suitable for various quantization levels, from BF16 to ultra-low-bit quantization (1-2 bits).

Natural Language ProcessingGgufMultiple Languages

GLM - 4 - Z1 - 32B - 0414 is a new - generation open - source large language model in the GLM family, with 32 billion parameters and the ability for in - depth thinking. Through cold - start and extended reinforcement learning training, it performs excellently in mathematics, code, and logic tasks and supports convenient local deployment.

Natural Language ProcessingTransformersMultiple Languages

CodeGeeX4-ALL-9B is a multilingual code generation model continuously trained based on GLM-4-9B. It is the open-source version of the CodeGeeX4 model series. This model performs the best among code generation models with less than 10 billion parameters. It supports comprehensive functions such as code completion, code interpreter, web search, function call, and repository-level code Q&A, achieving the best balance between inference speed and model performance.

Natural Language ProcessingGgufMultiple Languages

CodeGeeX4 is a multilingual code generation model continuously trained based on GLM-4-9B. It is currently the most powerful code generation model with less than 10 billion parameters. It supports comprehensive functions such as code completion, code generation, code explanation, web search, function call, and repository-level code Q&A, achieving the best balance between inference speed and model performance.

Natural Language ProcessingTransformersMultiple Languages

GLM-4-9B is the latest open-source pre-trained model released by Zhipu AI, surpassing Llama-3-8B in multiple benchmarks, supporting 128K context length and 26 languages.

Natural Language ProcessingTransformersMultiple Languages