Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!
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).
For full details of this model please read our paper.
Evaluation Results
Model
H6
Model Size
SOLAR-10.7B-Instruct-v1.0
74.20
~ 11B
mistralai/Mixtral-8x7B-Instruct-v0.1
72.62
~ 46.7B
01-ai/Yi-34B-200K
70.81
~ 34B
01-ai/Yi-34B
69.42
~ 34B
mistralai/Mixtral-8x7B-v0.1
68.42
~ 46.7B
meta-llama/Llama-2-70b-hf
67.87
~ 70B
tiiuae/falcon-180B
67.85
~ 180B
SOLAR-10.7B-v1.0
66.04
~11B
mistralai/Mistral-7B-Instruct-v0.2
65.71
~ 7B
Qwen/Qwen-14B
65.86
~ 14B
01-ai/Yi-34B-Chat
65.32
~34B
meta-llama/Llama-2-70b-chat-hf
62.4
~ 70B
mistralai/Mistral-7B-v0.1
60.97
~ 7B
mistralai/Mistral-7B-Instruct-v0.1
54.96
~ 7B
Usage Instructions
This model is pre-trained and is capable of just generating random text. To use it for chatting, you must fine-tune the model first.
Version
Make sure you have the correct version of the transformers library installed:
1text ="Hi, my name is "2inputs = tokenizer(text, return_tensors="pt")34outputs = model.generate(**inputs, max_new_tokens=64)5print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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.
bibtex
1@misc{kim2023solar,
2 title={SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling},
3 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},
4 year={2023},
5 eprint={2312.15166},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL}
8}
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