# Multilingual instruction

4-bit DWQ quantized version of the Llama 3.3 70B instruction-tuned model, optimized for efficient inference on the MLX framework

INT4 quantized version of Qwen3-30B-A3B, reducing disk and GPU memory requirements by 75% while maintaining high performance.

An efficient language model based on Qwen3-32B with FP8 dynamic quantization, significantly reducing memory requirements and improving computational efficiency

Llama-3.2-3B-Instruct GGUF is a 3B-parameter large language model released by Meta, utilizing IQ-DynamicGate technology for ultra-low-bit quantization (1-2 bits), optimizing inference performance while maintaining memory efficiency.

Built upon Qwen2.5-0.5B-Instruct, with the vocabulary transplanted from microsoft/phi-4, it can be used as a draft model for Phi-4.

Chocolatine-2-14B-Instruct-v2.0.3 is a large language model based on the Qwen-2.5-14B architecture, fine-tuned with DPO, specializing in French and English tasks, and excels in the French LLM leaderboard.

Phi-3-Medium-128K-Instruct is a lightweight open-source model with 14 billion parameters, focusing on high quality and strong reasoning capabilities, supporting a 128K context length.