# Low-resource deployment

DiffuCoder-7B-cpGRPO-8bit is a code generation model converted to MLX format, based on apple/DiffuCoder-7B-cpGRPO, and is specifically designed to provide developers with an efficient code generation tool.

A static quantization version of UniReason-Qwen3-14B-RL, suitable for text generation and mathematical reasoning research scenarios.

A static quantized version of the Google Gemma-3n-E2B model, offering various quantization types to balance model size and performance.

This is a quantized version of the Austral-70B-Winton model by Delta-Vector. Through quantization technology, it reduces the storage and computational resource requirements of the model while maintaining good performance, making it suitable for scenarios with limited resources.

A quantized version of Gama-12B, providing files of various quantization types, suitable for text generation tasks and supporting English and Portuguese.

Gama-12B is a large language model supporting multiple languages, offering various quantized versions to meet different performance and precision requirements.

The LongWriter-Zero-32B quantized model is based on the THU-KEG/LongWriter-Zero-32B base model, supports both Chinese and English, and is suitable for long context scenarios such as reinforcement learning and writing.

Skywork-SWE-32B is a large language model with 32B parameters. It is quantized by Llamacpp imatrix and can run efficiently in resource-constrained environments.

This is a quantized version of the NVIDIA AceReason - Nemotron - 1.1 - 7B model, which optimizes the model's running efficiency on different hardware while maintaining certain performance and quality.

This is the quantized version of OpenBuddy-R1-0528-Distill-Qwen3-32B-Preview0-QAT. With quantization technology, the model can run more efficiently under different hardware conditions.

This is a quantized model based on ONNX, which is the uint8 quantized version of Qwen/Qwen3-Embedding-0.6B. It reduces the model size while maintaining retrieval performance.

This is a text-to-video quantization model that undergoes quantization conversion based on a specific base model and supports various video generation tasks.

This is a quantized text-to-video model that converts the base model to the GGUF format and can be used in ComfyUI, providing more options for text-to-video generation.

A 6-bit quantized version converted from the DeepSeek-R1-0528-Qwen3-8B model, suitable for text generation tasks in the MLX framework.

Blitzar-Coder-4B-F.1 is an efficient multilingual coding model fine-tuned based on Qwen3-4B, supporting more than 10 programming languages and having excellent code generation, debugging, and reasoning capabilities.

This project provides the GGUF quantized file for the Echelon-AI/Med-Qwen2-7B model, supported by Featherless AI, aiming to enhance model performance and reduce operating costs.

Gemma 3n is a lightweight and state-of-the-art open-source multimodal model family launched by Google. It is built on the same research and technology as the Gemini model and supports text, audio, and visual inputs.

Bielik-11B-v2.6-Instruct is a large Polish language model developed by SpeakLeash and ACK Cyfronet AGH, fine-tuned based on Bielik-11B-v2, suitable for instruction following tasks.

Phi-3.5-mini-instruct is a lightweight and advanced open-source model built on the dataset used by Phi-3, focusing on high-quality, inference-rich data. It supports a 128K token context length and has powerful multilingual and long-context processing capabilities.

A quantized model based on DeepSeek-R1-0528, focusing on text generation tasks and providing a more efficient way of use.

A quantized version based on the infly/inf-o1-pi0 model, supporting multilingual text generation tasks, optimized with llama.cpp's imatrix quantization.

medgemma-4b-it is a multimodal model focused on the medical field, capable of processing image and text inputs, and suitable for multiple medical scenarios such as radiology and clinical reasoning.

This is a 4-bit quantized language model in MLX format, suitable for text generation tasks.

KernelLLM is a large language model developed by Facebook. This version is quantized using the llama.cpp tool with imatrix, offering multiple quantization options to suit different hardware requirements.

This is the statically quantized version of the Nellyw888/VeriReason-Qwen2.5-1.5B-grpo-small model, focusing on Verilog code generation and reasoning tasks.

Llamacpp imatrix quantized version based on a-m-team/AM-Thinking-v1 model, supporting multiple quantization types, suitable for text generation tasks.

Qwen3 is the latest generation of large language models in the Qwen series, offering a dense model with 0.6B parameters, achieving breakthrough progress in reasoning, instruction following, agent capabilities, and multilingual support.

This is a 12B-parameter large language model, processed with llama.cpp's imatrix quantization, offering multiple quantized versions to accommodate different hardware requirements.

This is a quantized version based on the Gryphe/Pantheon-Proto-RP-1.8-30B-A3B model, using llama.cpp for quantization, suitable for role-playing and text generation tasks.

This is a 4-bit quantized model converted from Qwen/Qwen3-30B-A3B to MLX format, suitable for text generation tasks.

This is a 1.5B parameter instruction fine-tuned model for Polish, developed based on the SpeakLeash Bielik series, suitable for text generation tasks.

The quantized version of Microsoft Phi-4-reasoning-plus, suitable for efficient text generation tasks on devices with limited resources.

Muyan-TTS is a text-to-speech (TTS) model, converted to GGUF format for use with llama.cpp.

This is the quantized version of the Qwen3-14B-abliterated model, quantized using llama.cpp's imatrix option, suitable for text generation tasks.

This repository contains GGUF format model files for Qwen/Qwen3-0.6B, quantized by TensorBlock's machines and compatible with llama.cpp.

A 1B-parameter language model specializing in English and Italian translation, offering multiple quantized versions in GGUF format.

Qwen2.5 is the latest series of the Tongyi Qianwen large model, including base models and instruction-tuned models with parameter scales ranging from 0.5B to 72B, showing significant improvements in areas such as code, mathematics, instruction following, and long text generation.

Qwen2-96M is a miniature language model based on the Qwen2 architecture, containing 96 million parameters and supporting a context length of 8192 tokens, suitable for English text generation tasks.

SmolVLM2-2.2B-Instruct is a vision-language model with a parameter scale of 2.2B, focusing on video text-to-text tasks and supporting English.

This project provides a static quantized version of the richinfoai/ritrieve_zh_v1 model. Through quantization, it reduces storage space and computational resource requirements while maintaining certain performance.