# 4-bit Quantized Inference

This is a 4-bit quantized version converted from the Qwen/Qwen3-0.6B model, suitable for efficient inference on the MLX framework.

This is a Mistral-7B instruction fine-tuned model optimized using Unsloth and Huggingface TRL library, achieving 2x training speed improvement

Llama 3.2 11B vision instruction fine-tuned model optimized with Unsloth, using 4-bit quantization technology, achieving 2x faster training speed

A fine-tuned version of the Qwen2-VL-7B model, trained using Unsloth and Huggingface's TRL library, achieving a 2x speed improvement.

A 4-bit quantized version of Meta-Llama-3.1-8B fine-tuned for bidirectional Bodo-English translation, optimized with Unsloth for faster training

Cogito-Maximus is an advanced text generation model optimized based on the Qwen2.5-72B instruction fine-tuning model. It uses Unsloth for accelerated training and the TRL fine-tuning framework, and is suitable for various text generation scenarios.

A text summarization model based on the Mistral 7B architecture, optimized for efficiency and performance using LoRA technology.

A BitsAndBytes NF4 quantized version based on Aria-sequential_mlp, suitable for image-to-text tasks with approximately 15.5 GB VRAM requirement.

This model is a Text2Cypher model fine-tuned based on google/gemma-2-9b-it, capable of converting natural language questions into Cypher query statements for Neo4j graph databases.

The AWQ quantized version of AceGPT 13B Chat, supporting English and Arabic, designed for general GPU users, offering efficient 4-bit quantized inference capabilities.