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---
license: apache-2.0
---
# **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](https://huggingface.co/upstage/SOLAR-10.7B-v1.0). 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](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) 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]]
# **Usage Instructions**
This model has been fine-tuned primarily for single-turn interactions, making it less suitable for multi-turn chat purposes.
### **Version**
Make sure you have the correct version of the transformers library installed:
```sh
pip install transformers==4.35.2
```
### **Loading the Model**
Use the following Python code to load the model:
```python
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**
```python
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> <|im_start|>user
Hello?<|im_end|>
<|im_start|>assistant
Hello, how can I assist you today?</s>
``` |