metadata

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 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.

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:

pip install transformers==4.35.2

Loading the Model

Use the following Python code to load the model:

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

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