# CPU optimization

Quantized version optimized for low-end hardware and CPU-only environments, achieving production-ready inference configurations under resource constraints

GLM-Z1-32B-0414 is a 32B-parameter multilingual text generation model supporting Chinese and English, released under the MIT license.

An instruction-tuned model based on OLMo-2-0325-32B-DPO, utilizing IQ-DynamicGate ultra-low bit quantization technology, optimized for memory-constrained environments.

all-MiniLM-L6-v2 is a compact and efficient sentence embedding model based on the MiniLM architecture, suitable for sentence similarity computation and feature extraction tasks.

Qwen2.5-14B-Instruct is an instruction-tuned model based on Qwen2.5-14B, supporting text generation tasks and suitable for chat and Q&A scenarios.

This is a base BERT model for computing compact embedding vectors of Russian sentences, developed based on cointegrated/rubert-tiny2, with the number of layers increased from 3 to 7.

CPU-optimized Danish language processing pipeline including tokenization, POS tagging, dependency parsing, named entity recognition, and other complete NLP functionalities

CPU-optimized Italian processing pipeline including tokenization, POS tagging, dependency parsing, named entity recognition, etc.

spaCy's CPU-optimized Russian processing pipeline, including tokenization, part-of-speech tagging, dependency parsing, named entity recognition, and other features