STEM-AI-mtl_-_phi-2-electrical-engineering-gguf

Quantization made by Richard Erkhov.

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phi-2-electrical-engineering - GGUF

• Model creator: https://huggingface.co/STEM-AI-mtl/
• Original model: https://huggingface.co/STEM-AI-mtl/phi-2-electrical-engineering/

Original model description:

license: other license_name: stem.ai.mtl license_link: LICENSE language:

• en tags:
• phi-2
• electrical engineering
• Microsoft datasets:
• STEM-AI-mtl/Electrical-engineering
• garage-bAInd/Open-Platypus task_categories:
  • question-answering
  • text-generation pipeline_tag: text-generation widget:
  • text: "Enter your instruction here" inference: true auto_sample: true inference_code: chat-GPTQ.py library_tag: transformers

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For the electrical engineering community

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.

• Developed by: STEM.AI
• Model type: Q&A and code generation
• Language(s) (NLP): English
• Finetuned from model: microsoft/phi-2

Direct Use

Q&A related to electrical engineering, and Kicad software. Creation of Python code in general, and for Kicad's scripting console.

Refer to microsoft/phi-2 model card for recommended prompt format.

Inference script

Standard

GPTQ format

Training Details

Training Data

Dataset related to electrical engineering: STEM-AI-mtl/Electrical-engineering 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.

In additionataset related to STEM and NLP: garage-bAInd/Open-Platypus

Training Procedure

LoRa script

A LoRa PEFT was performed on a 48 Gb A40 Nvidia GPU.

Model Card Authors

STEM.AI: [email protected]
William Harbec