# Open-source model

The Huihui AI Quantized Model is a quantized version of Magistral-Small-2506-abliterated, dedicated to making knowledge accessible to everyone.

DMind-1-mini is a lightweight text generation model suitable for various natural language processing tasks.

A large language model based on the Gemma architecture, specialized in Turkish text generation tasks.

OpenBA is an open-source 15-billion-parameter bilingual asymmetric sequence-to-sequence model, pre-trained from scratch.

This is a Transformers model released by Hugging Face. The specific functions and uses are to be supplemented.

Gemma is an advanced open-source model trained on high-quality datasets, supporting different context length requirements.

Open-source model based on Apache-2.0 license. Specific functions and usage require reference to the full documentation

InternLM-Math-Plus is the most advanced bilingual open-source large language model for mathematical reasoning, with functions such as solving, proving, verifying, and enhancing, providing strong support for the field of mathematical reasoning.

This is a transformers model hosted on Hugging Face Hub, with no specific functionality or purpose clearly stated.

MicroLlama is a 300-million-parameter Llama model pretrained by individual developer keeeeenw within a $500 budget, focusing on English text generation tasks.

A large-scale open-source Dutch language model based on Mistral 7B, significantly enhancing Dutch language comprehension and local knowledge coverage through incremental training on 10 billion Dutch text tokens.

LlongOrca 7B 16K is a large language model developed by Open-Orca based on the Llama architecture, supporting a 16K context length, suitable for text generation tasks.

Instruction-following model fine-tuned on LLaMA-7B, specifically optimized for code instruction scenarios

This model is released under the MIT License, with no specific details provided.

ParsGPT2 is a Persian version of the GPT-2 model, developed by the Hooshvare team for Persian text generation tasks.