README.md

---
license: apache-2.0
datasets:
- c-s-ale/alpaca-gpt4-data
- Open-Orca/OpenOrca
- Intel/orca_dpo_pairs
- allenai/ultrafeedback_binarized_cleaned
language:
- en
---

# **Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!**

**(This model is [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/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]]

# **Instruction Fine-Tuning Strategy**

We utilize state-of-the-art instruction fine-tuning methods including supervised fine-tuning (SFT) and direct preference optimization (DPO) [1].

We used a mixture of the following datasets
- c-s-ale/alpaca-gpt4-data (SFT)
- Open-Orca/OpenOrca (SFT)
- in-house generated data utilizing Metamath [2] (SFT, DPO)
- Intel/orca_dpo_pairs (DPO)
- allenai/ultrafeedback_binarized_cleaned (DPO)

where we were careful of data contamination by not using GSM8K samples when generating data and filtering tasks when applicable via the following list.
```python
filtering_task_list = [
    'task228_arc_answer_generation_easy',
    'ai2_arc/ARC-Challenge:1.0.0',
    'ai2_arc/ARC-Easy:1.0.0',
    'task229_arc_answer_generation_hard',
    'hellaswag:1.1.0', 
    'task1389_hellaswag_completion',
    'cot_gsm8k',
    'cot_gsm8k_ii',
    'drop:2.0.0',
    'winogrande:1.1.0'
]
```

Using the datasets mentioned above, we applied SFT and 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. NeurIPS.

[2] Yu, L., Jiang, W., Shi, H., Yu, J., Liu, Z., Zhang, Y., Kwok, J.T., Li, Z., Weller, A. and Liu, W., 2023. Metamath: Bootstrap your own mathematical questions for large language models. arXiv preprint arXiv:2309.12284.

# **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 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:

```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> ### 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** ###
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](mailto:contact@upstage.ai)