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license: apache-2.0 |
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# **Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!** |
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**(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.)** |
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# **Introduction** |
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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. |
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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. |
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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]). |
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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]] |
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# **Instruction Fine-Tuning Strategy** |
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We utilize state-of-the-art instruction fine-tuning methods including supervised fine-tuning (SFT) and direct preference optimization (DPO) [1]. |
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Using open source datasets with Alpaca- and OpenOrca-style and generated synthetic datasets, we apply iterative DPO training, a proprietary alignment strategy, to maximize the performance of our resulting model. |
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[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. |
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# **Evaluation Results** |
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| Model | H6 | Model Size | |
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|----------------------------------------|-------|------------| |
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| **SOLAR-10.7B-Instruct-v1.0** | **74.20** | **~ 11B** | |
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| mistralai/Mixtral-8x7B-Instruct-v0.1 | 72.62 | ~ 46.7B | |
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| 01-ai/Yi-34B-200K | 70.81 | ~ 34B | |
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| 01-ai/Yi-34B | 69.42 | ~ 34B | |
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| mistralai/Mixtral-8x7B-v0.1 | 68.42 | ~ 46.7B | |
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| meta-llama/Llama-2-70b-hf | 67.87 | ~ 70B | |
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| tiiuae/falcon-180B | 67.85 | ~ 180B | |
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| **SOLAR-10.7B-v1.0** | **66.04** | **~11B** | |
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| mistralai/Mistral-7B-Instruct-v0.2 | 65.71 | ~ 7B | |
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| Qwen/Qwen-14B | 65.86 | ~ 14B | |
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| 01-ai/Yi-34B-Chat | 65.32 | ~34B | |
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| meta-llama/Llama-2-70b-chat-hf | 62.4 | ~ 70B | |
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| mistralai/Mistral-7B-v0.1 | 60.97 | ~ 7B | |
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| mistralai/Mistral-7B-Instruct-v0.1 | 54.96 | ~ 7B | |
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# **Usage Instructions** |
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This model has been fine-tuned primarily for single-turn conversation, making it less suitable for multi-turn conversations such as chat. |
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### **Version** |
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Make sure you have the correct version of the transformers library installed: |
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```sh |
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pip install transformers==4.35.2 |
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``` |
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### **Loading the Model** |
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Use the following Python code to load the model: |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("Upstage/SOLAR-10.7B-Instruct-v1.0") |
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model = AutoModelForCausalLM.from_pretrained( |
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"Upstage/SOLAR-10.7B-Instruct-v1.0", |
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device_map="auto", |
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torch_dtype=torch.float16, |
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) |
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``` |
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### **Conducting Single-Turn Conversation** |
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```python |
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conversation = [ {'role': 'user', 'content': 'Hello?'} ] |
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prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True) |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, use_cache=True, max_length=4096) |
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output_text = tokenizer.decode(outputs[0]) |
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print(output_text) |
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``` |
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Below is an example of the output. |
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``` |
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<s> ### User: |
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Hello? |
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### Assistant: |
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Hello, how can I assist you today? Please feel free to ask any questions or request help with a specific task.</s> |
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``` |
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### **The Upstage AI Team** ### |
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Upstage is creating the best LLM and DocAI. Please find more information at https://upstage.ai |
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### **Contact Us** ### |
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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) |
