SOLAR 10.7B is a large language model (LLM) that utilizes a novel depth up-scaling (DUS) methodology to achieve superior performance compared to models many times its size. Built on the Llama architecture, this 10.7-billion parameter model represents a significant advancement in efficient model scaling techniques, as detailed in the associated research paper.
Architecture and Technical Innovation
The model's architecture is based on a 32-layer Llama 2 structure, initialized with Mistral 7B weights. The key innovation lies in its depth up-scaling (DUS) approach, which involves depthwise scaling by duplicating and concatenating portions of the model to increase the number of layers, followed by continued pre-training to recover and enhance performance. Unlike other scaling methods such as Mixture-of-Experts (MoE), DUS avoids complex architectural modifications, making it more efficient and easily adaptable to existing training and inference frameworks.
Training and Fine-tuning
SOLAR 10.7B underwent a sophisticated two-stage fine-tuning process:
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Instruction tuning: This phase utilized open-source datasets including Alpaca-GPT4 and OpenOrca, along with a synthetic math QA dataset created by rephrasing questions and answers from the Math dataset.
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Alignment tuning: This stage employed direct preference optimization (sDPO) with open-source datasets and a synthetic math alignment dataset derived from the instruction-tuning phase.
The training process also incorporated model merging techniques to further enhance performance, as documented in the model's Hugging Face repository.
Performance and Benchmarks
SOLAR 10.7B demonstrates remarkable performance across various benchmarks, particularly in its instruction-tuned variant (SOLAR-10.7B-Instruct-v1.0). On the H6 benchmark, the instruction-tuned version achieves a score of 74.20, surpassing significantly larger models including:
- Mixtral-8x7B-Instruct-v0.1 (72.62)
- Yi-34B-200K (70.81)
- Llama-2-70b-hf (67.87)
- Falcon-180B (67.85)
Even the base model (SOLAR-10.7B-v1.0) performs impressively with a score of 66.04, outperforming many comparable or larger models. The model shows particular strength in tasks such as ARC, HellaSwag, MMLU, TruthfulQA, Winogrande, and GSM8K.
Licensing and Availability
The base SOLAR-10.7B-v1.0 model is released under the Apache-2.0 license, making it accessible for both research and commercial applications. However, the instruction-tuned variant (SOLAR-10.7B-Instruct-v1.0) is licensed under cc-by-nc-4.0 due to the inclusion of non-commercial datasets in its fine-tuning process.
Usage and Implementation
The model can be implemented using the transformers library (version 4.35.2 or later recommended). While the base model is primarily designed for text generation, it performs best after fine-tuning for specific applications. The instruction-tuned variant is particularly well-suited for chat and instruction-following tasks.
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