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--- |
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license: llama3 |
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datasets: |
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- arcee-ai/EvolKit-20k |
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language: |
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- en |
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct |
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library_name: transformers |
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--- |
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<div align="center"> |
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<img src="https://i.ibb.co/r072p7j/eopi-ZVu-SQ0-G-Cav78-Byq-Tg.png" alt="Llama-3.1-SuperNova-Lite" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;"> |
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</div> |
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## Overview |
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Llama-3.1-SuperNova-Lite is an 8B parameter model developed by Arcee.ai, based on the Llama-3.1-8B-Instruct architecture. It is a distilled version of the larger Llama-3.1-405B-Instruct model, leveraging offline logits extracted from the 405B parameter variant. This 8B variation of Llama-3.1-SuperNova maintains high performance while offering exceptional instruction-following capabilities and domain-specific adaptability. |
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The model was trained using a state-of-the-art distillation pipeline and an instruction dataset generated with [EvolKit](https://github.com/arcee-ai/EvolKit), ensuring accuracy and efficiency across a wide range of tasks. For more information on its training, visit blog.arcee.ai. |
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Llama-3.1-SuperNova-Lite excels in both benchmark performance and real-world applications, providing the power of large-scale models in a more compact, efficient form ideal for organizations seeking high performance with reduced resource requirements. |
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# Evaluations |
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We will be submitting this model to the OpenLLM Leaderboard for a more conclusive benchmark - but here are our internal benchmarks using the main branch of lm evaluation harness: |
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| Benchmark | SuperNova-Lite | Llama-3.1-8b-Instruct | |
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|-------------|----------------|----------------------| |
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| IF_Eval | 81.1 | 77.4 | |
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| MMLU Pro | 38.7 | 37.7 | |
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| TruthfulQA | 64.4 | 55.0 | |
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| BBH | 51.1 | 50.6 | |
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| GPQA | 31.2 | 29.02 | |
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The script used for evaluation can be found inside this repository under /eval.sh, or click [here](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite/blob/main/eval.sh) |
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# note |
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This readme will be edited regularly on September 10, 2024 (the day of release). After the final readme is in place we will remove this note. |
