SOLAR 10.7B V1.0

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

SOLAR-10.7B is an advanced large language model with 10.7 billion parameters. It offers superior performance in various NLP tasks, outshining many models with up to 30B parameters. It's compact yet powerful, and is an excellent choice for fine - tuning.

🚀 Quick Start

This model is pre - trained and can generate random text. To use it for chatting, you need to fine - tune the model first.

Version

Ensure you have the correct version of the transformers library installed:

pip install transformers==4.35.2

Loading the Model

Use the following Python code to load the model:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Upstage/SOLAR-10.7B-v1.0")
model = AutoModelForCausalLM.from_pretrained(
    "Upstage/SOLAR-10.7B-v1.0",
    device_map="auto",
    torch_dtype=torch.float16,
)

Generating Text

To generate text, use the following Python code:

text = "Hi, my name is "
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

✨ Features

• High - Performance: SOLAR - 10.7B outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model.
• Fine - Tuning Friendly: It offers robustness and adaptability for fine - tuning needs. Simple instruction fine - tuning using the SOLAR - 10.7B pre - trained model yields significant performance improvements.

📚 Documentation

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 ([SOLAR - 10.7B - Instruct - v1.0](https://huggingface.co/upstage/SOLAR - 10.7B - Instruct - v1.0)).

For full details of this model please read our paper.

Evaluation Results

📄 License

• [upstage/SOLAR - 10.7B - v1.0](https://huggingface.co/upstage/SOLAR - 10.7B - v1.0): apache - 2.0
• [upstage/SOLAR - 10.7B - Instruct - v1.0](https://huggingface.co/upstage/SOLAR - 10.7B - Instruct - v1.0): cc - by - nc - 4.0
  • Since some non - commercial datasets such as Alpaca are used for fine - tuning, we release fine - tuned model as cc - by - nc - 4.0.

🔧 How to Cite

Please cite this model using this format.

@misc{kim2023solar,
      title={SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up - Scaling}, 
      author={Dahyun Kim and Chanjun Park and Sanghoon Kim and Wonsung Lee and Wonho Song and Yunsu Kim and Hyeonwoo Kim and Yungi Kim and Hyeonju Lee and Jihoo Kim and Changbae Ahn and Seonghoon Yang and Sukyung Lee and Hyunbyung Park and Gyoungjin Gim and Mikyoung Cha and Hwalsuk Lee and Sunghun Kim},
      year={2023},
      eprint={2312.15166},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

📞 The Upstage AI Team

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📧 Contact Us

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