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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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library_tag: transformers |
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--- |
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# Model Card for Model ID |
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This is 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 model card for recommended prompt format. |
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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 |
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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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Combined with |
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Dataset related to STEM and NLP: 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 |
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### Inference example |
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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 |
