--- 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. 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: ```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-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: ```python 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 ](mailto:contact@upstage.ai)