metadata

language: en
tags:
  - log-analysis
  - pythia
  - hdfs
license: mit
datasets:
  - honicky/log-analysis-hdfs-preprocessed
metrics:
  - cross-entropy
  - perplexity
base_model: EleutherAI/pythia-70m

pythia-70m-hdfs-logs

Fine-tuned Pythia-14m model for HDFS log analysis, specifically for anomaly detection.

Model Description

This model is fine-tuned from EleutherAI/pythia-70m for analyzing HDFS log sequences. It's designed to understand and predict patterns in HDFS log data so that we can detect anomalies using the perplexity of the log sequence. THhe HDFS sequence is handy because it has labels so we can use it to validate that the model can predict anomalies.

We will use this model to understand the ability of a small model to predict anomalies in a specific dataset. We will study model scale and experiment with tokenization, intialization, data set size, etc. to find a configuration that is minimal in size and fast, but can effectively predict anomalies. We will then attempt build a model that is more robust to different log formats.

Huggingface Model: honicky/pythia-70m-hdfs-logs

Training Details

Base model: EleutherAI/pythia-70m
Dataset: https://zenodo.org/records/8196385/files/HDFS_v1.zip?download=1 + preprocessed data at honicky/log-analysis-hdfs-preprocessed
Batch size: 32
Max sequence length: 405
Learning rate: 0.0001
Training steps: 16000
Weights and Biases run: https://wandb.ai/honicky/log-analysis-pythia/runs/dwb96ojk

Special Tokens

Added <|sep|> token for event ID separation

Intended Use

This model is intended for:

Analyzing HDFS log sequences
Detecting anomalies in log patterns
Understanding system behavior through log analysis

Limitations

Model is specifically trained on HDFS logs and may not generalize to other log formats
Limited to the context window size of 405 tokens