README.md

metadata

license: mit
datasets:
  - adeeshajayasinghe/devops-guide-demo
metrics:
  - accuracy
base_model:
  - microsoft/phi-2
new_version: microsoft/phi-2
pipeline_tag: text-generation
library_name: transformers
tags:
  - code
  - text-generation-inference

DevOps Mastermind Model

This repository hosts the DevOps Mastermind model, a pre-trained model based on microsoft/phi-2 with modifications tailored for specialized DevOps knowledge tasks. The model is designed to support various downstream tasks, such as code generation, documentation assistance, and knowledge inference in DevOps domains.

Model Details

• Base Model: microsoft/phi-2
• Purpose: Enhanced with additional training and modifications for DevOps and software engineering contexts.
• Files Included:
  • config.json: Model configuration.
  • pytorch_model.bin: The primary model file containing weights.
  • tokenizer.json: Tokenizer for processing text inputs.
  • added_tokens.json: Additional tokens specific to DevOps vocabulary.
  • generation_config.json: Generation configuration for text generation tasks.
  • Other auxiliary files required for model usage and compatibility.

Usage

To load and use this model in your code, run the following commands:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "kavinduc/devops-mastermind"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)

# Example usage
input_text = "Explain how to set up a CI/CD pipeline"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)