SOLAR

About

What SOLAR is

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.) Introduction We introduce SOLAR-10.7B, an advanced large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B. We present a methodology for scaling LLMs called depth up-scaling (DUS) , which encompasses architectural modifications and continued pretraining. In other words, we integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model. 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. 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. For full details of this model please read our paper. 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/orcadpopairs (DPO) - allenai/ultrafeedbackbinarizedcleaned (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. 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

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys solar for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (solar below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"solar","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Papers

Licence

Commercial licence needed

Sources

Weights & code

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