Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!
Introduction
We introduce the first 10.7 billion (B) parameter model, SOLAR-10.7B. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B.
We developed the Depth Up-Scaling technique. Built on the Llama2 architecture, SOLAR-10.7B incorporates the innovative Upstage Depth Up-Scaling. We then integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.
Depth-Upscaled 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 ([link to be updated soon]). 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. [[link to be updated soon]]
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:
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))
The Upstage AI Team
Upstage is creating the best LLM and DocAI. Please find more information at https://upstage.ai
Contact Us
Any questions and suggestions, please use the discussion tab. If you want to contact us directly, drop an email to contact@upstage.ai
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