license: apache-2.0
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. Detailed description to be added.)
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]]
Training Strategy
We utilize state-of-the-art instruction fine-tuning methods including supervised fine-tuning (SFT) and direct preference optimization (DPO) [1]. Using open source datasets with Alpaca- and OpenOrca-style and generated synthetic datasets, we apply an iterative DPO training, a proprietary alignment strategy, to maximize the performance of our resulting model.
[1] Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C.D. and Finn, C., 2023. Direct preference optimization: Your language model is secretly a reward model. arXiv preprint arXiv:2305.18290.
Usage Instructions
This model has been fine-tuned primarily for single-turn conversation, making it less suitable for multi-turn conversations such as chat.
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-Instruct-v1.0")
model = AutoModelForCausalLM.from_pretrained(
"Upstage/SOLAR-10.7B-Instruct-v1.0",
device_map="auto",
torch_dtype=torch.float16,
)
Conducting Single-Turn Conversation
conversation = [ {'role': 'user', 'content': 'Hello?'} ]
prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, use_cache=True, max_length=4096) output_text = tokenizer.decode(outputs[0])
print(output_text)
Below is an example of the output.
<s> ### User:
Hello?
### Assistant:
Hello, how can I assist you today? Please feel free to ask any questions or request help with a specific task.</s>
The Upstage AI Team
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