SOLAR 10.7B Instruct v1.0 EU AI Act Compliance Profile

Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!

(This model is upstage/SOLAR-10.7B-v1.0 fine-tuned version for single-turn conversation.)

Introduction

We introduce SOLAR-10.7B, an advanced large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B.

We present a methodology for scaling LLMs called depth up-scaling (DUS) , which encompasses architectural modifications and continued pretraining. In other words, we integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.

SOLAR-10.7B has remarkable performance. It outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model. For detailed information, please refer to the experimental table. Solar 10.7B is an ideal choice for fine-tuning. SOLAR-10.7B offers robustness and adaptability for your fine-tuning needs. Our simple instruction fine-tuning using the SOLAR-10.7B pre-trained model yields significant performance improvements.

For full details of this model please read our paper.

Instruction Fine-Tuning Strategy

We utilize state-of-the-art instruction fine-tuning methods including supervised fine-tuning (SFT) and direct preference optimization (DPO) [1].

We used a mixture of the following datasets

• c-s-ale/alpaca-gpt4-data (SFT)
• Open-Orca/OpenOrca (SFT)
• in-house generated data utilizing Metamath [2] (SFT, DPO)
• Intel/orca_dpo_pairs (DPO)
• allenai/ultrafeedback_binarized_cleaned (DPO)

Your obligations as a SOLAR 10.7B Instruct v1.0 deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Languages

Library

Base Model