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--- |
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license: other |
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license_name: stem.ai.mtl |
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license_link: LICENSE |
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language: |
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- en |
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tags: |
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- phi-2 |
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- electrical engineering |
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- Microsoft |
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datasets: |
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- STEM-AI-mtl/Electrical-engineering |
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- garage-bAInd/Open-Platypus |
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task_categories: |
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- question-answering |
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- text-generation |
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pipeline_tag: text-generation |
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widget: |
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- text: "Enter your instruction here" |
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inference: true |
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auto_sample: true |
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inference_code: chat-GPTQ.py |
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library_tag: transformers |
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--- |
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# Model Card for Model ID |
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A unique, deployable and efficient 2.7 billion parameters model in the field of electrical engineering. This repo contains the adapters from the LoRa fine-tuning of the phi-2 model from Microsoft. It was trained on the STEM-AI-mtl/Electrical-engineering dataset combined with garage-bAInd/Open-Platypus. |
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- **Developed by:** STEM.AI |
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- **Model type:** Q&A and code generation |
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- **Language(s) (NLP):** English |
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- **Finetuned from model:** microsoft/phi-2 |
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### Direct Use |
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Q&A related to electrical engineering, and Kicad software. Creation of Python code in general, and for Kicad's scripting console. |
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Refer to [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) model card for recommended prompt format. |
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### Inference script |
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[Standard](https://github.com/STEM-ai/Phi-2/blob/4eaa6aaa2679427a810ace5a061b9c951942d66a/chat.py) |
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[GPTQ format](https://github.com/STEM-ai/Phi-2/blob/ab1ced8d7922765344d824acf1924df99606b4fc/chat-GPTQ.py) |
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## Training Details |
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### Training Data |
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Dataset related to electrical engineering: [STEM-AI-mtl/Electrical-engineering](https://huggingface.co/datasets/STEM-AI-mtl/Electrical-engineering) |
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It is composed of queries, 65% about general electrical engineering, 25% about Kicad (EDA software) and 10% about Python code for Kicad's scripting console. |
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In additionataset related to STEM and NLP: [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) |
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### Training Procedure |
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[LoRa script](https://github.com/STEM-ai/Phi-2/raw/4eaa6aaa2679427a810ace5a061b9c951942d66a/LoRa.py) |
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A LoRa PEFT was performed on a 48 Gb A40 Nvidia GPU. |
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## Model Card Authors |
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STEM.AI: stem.ai.mtl@gmail.com\ |
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[William Harbec](https://www.linkedin.com/in/william-harbec-56a262248/) |
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