# Multi-task generalization

AceMath-RL-Nemotron-7B is a mathematical reasoning model trained entirely through reinforcement learning. It is trained based on Deepseek-R1-Distilled-Qwen-7B and performs excellently in mathematical reasoning tasks. It also has certain generalization ability in coding tasks.

This is an experimental small reasoning model designed to run on 8GiB consumer-grade GPUs with general inference capabilities. Through supervised fine-tuning (SFT) and high-quality reasoning trajectory training, the model can generalize its reasoning abilities to multiple tasks.

Bamba-9B-v2 is a decoder-only language model built on the Mamba-2 architecture, focusing on text generation tasks and outperforming Llama 3.1 8B in performance.

T0++ is a natural language processing model based on the T5 architecture, achieving zero-shot task generalization through multi-task prompt training, outperforming GPT-3 on various NLP tasks while being more compact.

A fine-tuned model based on Llama-3-8b, optimized using the Gutenberg DPO dataset, suitable for text generation tasks.

A RoBERTa-large model designed for efficient zero-shot classification, trained on commercially friendly data, capable of performing text classification tasks without training data.

WizardLM-13B V1.2 is a large language model trained on Llama-2 13b, focusing on complex instruction-following capabilities.