# Efficient quantization

A quantized version based on the Baidu ERNIE-4.5-0.3B-PT model, optimized through the llama.cpp tool to reduce the model size and improve the running efficiency.

This is the quantization version of BAAI's RoboBrain2.0-7B model, which is quantized through llama.cpp and provides various quantization types to meet different hardware requirements.

StrawberryLemonade-L3-70B-v1.0 is a quantized large language model designed to run efficiently under different hardware conditions.

Wan14BT2VFusionX is a text-to-video generation model that supports video generation through the ComfyUI - GGUF custom node.

Qwen3 is the latest version of the Tongyi Qianwen series of large language models, offering a range of dense and Mixture of Experts (MoE) models. Based on large-scale training, Qwen3 has achieved breakthrough progress in reasoning capabilities, instruction following, agent functionalities, and multilingual support.

Gemma 3 is a lightweight, state-of-the-art multimodal open-source model launched by Google, capable of processing text and image inputs and generating text outputs.

This is a 3B-parameter language model optimized for Korean, converted to GGUF format for compatibility with llama.cpp.

Gemma 3 is a lightweight open-source multimodal model family launched by Google, built on the same technology as Gemini, supporting text and image inputs and generating text outputs.

The quantized version of the Command R model released by CohereForAI, aiming to make knowledge accessible to the public.

Gemma 3.4B IT is a lightweight open-source large language model released by Google. Based on a parameter scale of 3.4B, it is suitable for dialogue and instruction following tasks.

A quantized version of the multilingual embedding model developed by the IBM Granite team, supporting text embedding tasks in 17 languages, suitable for scenarios such as retrieval and information extraction.

IBM Granite 8B code instruction model, supporting a context length of 128k, focusing on code generation and instruction understanding tasks.

Based on the Qwen2.5-Coder-3B-Instruct model, quantization processing is performed, providing an efficient and convenient solution for code generation and dialogue interaction.

This is a 7B parameter model based on the Mistral architecture, optimized through quantization, offering various GGUF quantization versions to meet different hardware requirements.

The 4-bit quantized version of Molmo-7B-O, significantly reducing the memory requirement and suitable for environments with limited resources.

The GGUF format version of Llama-3.2-1B-Instruct, providing broader support and better performance.

This is a version of the openchat/openchat-3.6-8b-20240522 model after Llama.cpp imatrix quantization. It provides files of different quantization types, making it convenient for users to download and use according to their needs.

The GGUF quantized version of DeepSeek-V2-Lite, processed by Llama.cpp imatrix quantization, reduces storage and computing resource requirements and facilitates deployment.

The GGUF quantized version of DeepSeek-V2-Chat, suitable for local deployment and operation.

A model fine-tuned on a specific Turkish dataset, capable of accurately answering information in Turkish and providing strong support for Turkish-related text generation tasks.