# GGUF Efficient Inference

This is a Chinese language model converted from canopylabs/3b-zh-ft-research_release to GGUF format, suitable for text generation tasks.

This is a GGUF-format German language model converted from canopylabs/3b-de-ft-research_release, suitable for text generation tasks.

This is a GGUF format model converted from canopylabs/3b-es_it-ft-research_release, supporting Spanish and Italian.

Qwen2.5-VL-72B-Instruct is a 72B-parameter multimodal large model that supports vision-language tasks, capable of understanding and generating text related to images.

This model is a GGUF format conversion of BAAI/bge-reranker-v2-m3, designed for text ranking tasks with multilingual support.

An 8B-parameter open-source large language model released by NVIDIA, based on the Llama-3 architecture, offering multiple quantization versions

This is the GGUF quantized version of Google's Gemma 3B model, suitable for local deployment and inference.

This is a business domain-adapted model based on Mistral-7B, converted to GGUF format for use in llama.cpp.

This model is a GGUF format conversion based on openGPT-X/Teuken-7B-instruct-research-v0.4, supporting multilingual text generation tasks.

NoticIA-7B is a Spanish-language news summarization model, specialized in processing news content and generating summaries.

This model is converted from BAAI/bge-reranker-large into GGUF format for reranking tasks, supporting both Chinese and English.

This model is converted from BAAI/bge-reranker-v2-m3 into GGUF format for text reranking tasks, supporting multiple languages.

This is a GGUF format model converted from MBZUAI/LaMini-Flan-T5-248M, suitable for text generation tasks.

This model is a GGUF format conversion based on xtuner/llava-llama-3-8b-v1_1, supporting multimodal processing of images and text.

WizardLM-2-8x22B-GGUF is the GGUF quantized version of Microsoft's WizardLM-2-8x22B model, supporting multiple bit quantizations, suitable for text generation tasks.

A large language model based on LLaMa2-13b, supporting Traditional Chinese, in GGUF format