# Low-resource inference optimization

Kodify-Nano-GGUF is the GGUF version of the Kodify-Nano model, optimized for CPU/GPU inference. It is a lightweight large language model suitable for code development tasks.

An optimized version based on the Qwen3-30B-A3B Mixture of Experts model, improving speed by reducing the number of active experts, supporting 64k context length, and suitable for various text generation tasks.

GGUF quantized version based on Qwen3-30B-A3B Mixture of Experts model, extended to 128k context, optimized with NEO Imatrix quantization technology, supporting multilingual and multitask processing.

This is the quantized version of the original model meta-llama/Llama-4-Scout-17B-16E-Instruct, optimized with int4 quantization technology, suitable for multilingual tasks.

Llama-3.2-11B-Vision-Instruct is a multilingual vision-language model that can be used for image-text to text conversion tasks.

This is NVIDIA's AWQ 4-bit quantized version of the Llama-3.1-Nemotron-70B-Instruct model, customized based on Meta's Llama-3.1-70B-Instruct, focusing on improving the usefulness of generated responses.

A 7B-parameter large language model based on the Mistral architecture, trained with DPO (Direct Preference Optimization), demonstrating excellent performance in multiple benchmarks

speechless-coder-ds-6.7b is a large language model fine-tuned based on deepseek-ai/deepseek-coder-6.7b, focusing on improving code generation and programming assistance capabilities.

GenZ is an advanced large language model fine-tuned from Meta's open-source Llama V2 70B parameter model, designed to provide high-performance text generation capabilities for the open-source community.