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@@ -42,15 +42,13 @@ Refer to microsoft/phi-2 model card for recommended prompt format.
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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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-
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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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@@ -61,6 +59,6 @@ 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
 
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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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  ### 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)