---
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>

```