metadata

library_name: transformers
tags:
  - chaos-engineering
  - IT-infrastructure
  - H.A.N.D.S
  - python
  - AI

Model Card for phi-2-chaos-gen

This model card describes the phi-2-chaos-gen, a fine-tuned version of the PHI-2 model, specialized in generating insights and strategies for chaos engineering in IT infrastructures.

Model Details

Model Description

The phi-2-chaos-gen is a fine-tuned version of the PHI-2 model, developed to assist in chaos engineering for IT infrastructures. It utilizes a unique methodology called H.A.N.D.S (Hardware, Application, Network, Data, Security) to generate relevant strategies and insights. This model aims to provide comprehensive chaos engineering solutions, focusing on each aspect of IT infrastructure.

Developed by: Webnizam
Model type: Text generation
Language(s) (NLP): English
License: MIT License
Finetuned from model: Microsoft's PHI-2

Model Sources

Repository: webnizam/phi-2-chaos-gen

Uses

Direct Use

The model can be directly used by IT professionals and organizations to generate strategies and insights for chaos engineering in their IT infrastructure, focusing on hardware, application, network, data, and security aspects.

Bias, Risks, and Limitations

The model, while powerful, may have limitations in understanding highly specialized or newly emerging IT concepts. Users should verify the model's recommendations with current IT standards and practices.

Recommendations

It's recommended to use this model as a starting point or a complement to existing chaos engineering practices, not as a sole source of truth.

How to Get Started with the Model

To use the phi-2-chaos-gen model, follow these steps:

Installation: Install the transformers library using pip:
```
pip install transformers
```

Loading the Model: Load the phi-2-chaos-gen model using the transformers package. Ensure you have an internet connection as the model will be downloaded the first time you run this code.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

base_model_id = "microsoft/phi-2"
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,  # Phi2, same as before
    device_map="auto",
    trust_remote_code=True,
    load_in_8bit=True,
    torch_dtype=torch.float16,
)

base_model.config.use_cache = True
eval_tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token=True, trust_remote_code=True, use_fast=False)

Using the Model: You can now use the model to generate text. For example:

from peft import PeftModel

ft_model = PeftModel.from_pretrained(base_model, "Falconsai/phi-2-chaos")

eval_prompt = """
Give me a list of chaos-engineering scenarios to execute in an IT infrastructure, list the results in H.A.N.D.S. i need it as a list with sub headings and contents.
"""
model_input = eval_tokenizer(eval_prompt, return_tensors="pt").to("cuda")

ft_model.eval()
with torch.no_grad():
    print(eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=300, repetition_penalty=1.11)[0], skip_special_tokens=True))

Results

The model showed proficiency in generating relevant and practical strategies for different scenarios within the scope of H.A.N.D.S.

Technical Specifications

Model Architecture and Objective

The model follows the architecture of the PHI-2 model, fine-tuned for text generation in the context of chaos engineering.