# Low-Resource Deployment

This is a static quantized version of the chandar-lab/NeoBERT model, aiming to reduce model storage space and computational resource requirements.

This is a multimodal large language model quantized with A8W8, based on Qwen2.5-VL-7B-Instruct, supporting vision and language tasks.

Static quantized version of Qwen2-Audio-7B-Instruct model, supporting English audio-to-text conversion tasks

This is a quantized version of the Sarvam-m model, supporting text generation tasks in multiple Indian languages and English.

This is the GGUF quantized conversion version of the Wan-AI/Wan2.1-VACE-14B model, primarily designed for text-to-video generation tasks.

Qwen3 is the latest generation of large language models in the Tongyi Qianwen series, offering a complete combination of dense models and Mixture of Experts (MoE) models. Based on large-scale training, Qwen3 achieves breakthrough progress in reasoning capabilities, instruction following, agent functions, and multilingual support.

A quantized version of Qwen2.5-VL-7B-Instruct, using llama.cpp for quantization, supporting multimodal tasks such as image-to-text conversion.

This is the quantized version of the NVIDIA OpenCodeReasoning-Nemotron-32B-IOI model, processed using llama.cpp for quantization, suitable for code reasoning tasks.

This is the quantized version of the nomic-ai/nomic-embed-code model, using llama.cpp for imatrix quantization, suitable for code embedding and feature extraction tasks.

This is a quantized version of Microsoft's Phi-4-reasoning model, optimized using llama.cpp for inference tasks and supporting multiple quantization options.

Qwen3-4B is a GGUF format model based on Qwen3-4B-Base, suitable for text generation tasks.

This is a multilingual text-to-speech model supporting 11 languages including Chinese, English, German, etc., converted from HKUSTAudio/Llasa-1B-Multilingual.

Qwen3-1.7B-Q8_0-GGUF is a GGUF-format model converted from Qwen/Qwen3-1.7B, supporting text generation tasks with multilingual capabilities and efficient reasoning.

ChatTime-1-7B-Base is a foundational model specialized in time series forecasting, supporting multimodal time series processing.

ChatTime-1-7B-Chat is a multimodal foundation model specialized in time series forecasting, built on a 7B parameter scale.

The Llamacpp imatrix quantized version of Qwen3-0.6B provided by the Qwen team, quantized using llama.cpp, supports running in LM Studio or projects based on llama.cpp.

GGUF quantized versions of the Lightricks/LTX-Video model, including development and distilled editions, designed for text-to-video generation tasks.

This project provides weighted/matrix quantized versions of the llava-1.5-13b-hf model, including various quantization types to meet the usage requirements in different scenarios.

INT4 quantized version based on the gemma-3-4b-it model, significantly reducing storage and computational resource requirements

A 4-bit quantized model optimized based on Qwen2.5-3B-Instruct, supporting both Chinese and English, with chain-of-thought control and tool invocation capabilities.

Beaver-7B-v3.0 is a 7B-parameter large language model based on the LLaMA architecture, focusing on safety and human feedback reinforcement learning (RLHF).

This is a statically quantized version of a T5-based news summarization model, supporting English text summarization tasks.

Orpheus is a high-performance text-to-speech model, fine-tuned to achieve natural and emotionally rich speech synthesis. This repository hosts the 8-bit quantized version of the 3-billion-parameter model, optimizing operational efficiency while maintaining high-quality output.

Static quantized version of STEVE-R1-7B-SFT, supporting multiple quantization levels for different hardware requirements

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

Heron-NVILA-Lite-2B is a vision-language model based on the NVILA-Lite architecture, specifically trained for Japanese, supporting image-text interaction tasks in both Japanese and English.

Qwen2.5-VL-7B-Instruct is a multimodal vision-language model that supports image-text generation tasks.

The Trillion-7B Preview is a multilingual large language model supporting English, Korean, Japanese, and Chinese. It outperforms other 7B-scale models in computational efficiency and performance.

A quantized version based on Google Gemma 3B model, optimized using llama.cpp, supporting multiple quantization levels, suitable for text generation tasks.

This is a text ranking model used for reordering and scoring texts.

A 0.5B parameter text reranking model based on Qwen2.5 architecture, efficiently trained using Unsloth and TRL libraries

Gemmasutra-Small-4B-v1 is a 4B-parameter text generation model, quantized based on llama.cpp, suitable for various quantization version choices.

Terjman-Nano-v2.0 is a Transformer-based English-Moroccan dialect translation model with 77M parameters, optimized for high-quality and precise translation.

Quantized version of Qwen2.5-VL-7B-Instruct, supporting vision-text input and text output, with weights quantized to INT4 and activations to FP16.

This is a GGUF format model converted from DISLab/SummLlama3.2-3B, primarily used for text summarization tasks.

Terjman Large-v2.0 is a Transformer-based English-Moroccan dialect translation model with significantly improved performance, comparable to commercial models.

A quantized version of the multimodal model based on Qwen2-VL-7B-Instruct, supporting image-text-to-text tasks with various quantization levels.

The GGUF format version of the InternLM3-8B-Instruct model, suitable for the llama.cpp framework and supporting multiple quantization versions.

Vintern-1B-v3.5 is a multimodal large language model fine-tuned based on InternVL2.5-1B, specializing in Vietnamese text processing, excelling in OCR and understanding Vietnamese-specific documents.

A 32B-parameter large language model based on llama.cpp, specializing in text generation tasks for chemistry, biology, climate, and medical fields