The Mistral 7B - Time Series Predictor is a fine-tuned large language model designed to analyze server performance metrics and forecast potential failures. It processes time-series data and predicts failure probabilities, offering actionable insights for predictive maintenance and operational risk assessment.
Model Details
Model Description
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Developed by: Sivakrishna Yaganti and Shankar Jayaratnam
Funded by: Esperanto Technologies
Model type: Causal Language Model, fine-tuned for time-series forecasting
Finetuned from model: Mistral 7B
Model Sources [optional]
Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]
Uses
Direct Use
The model can be directly used to:
Forecast server health based on time-series metrics like temperature, power consumption, utilization and throughput.
Predict potential causes of failures using historical data.
Downstream Use [optional]
The model is ideal for integration into platforms such as Splunk and Grafana to:
Monitor server health in real-time.
Support decision-making in preventive maintenance.
Out-of-Scope Use
This model is not designed for general time-series forecasting outside server health monitoring.
It may not perform well on non-server-related data or domains significantly different from its training dataset.
Bias, Risks, and Limitations
Bias:
Performance may vary on datasets with metrics significantly different from those in the training data.
Predictions are most accurate when used within the context of server health monitoring.
Risks
Relying solely on the model without validating its predictions may result in inaccurate failure forecasts.
Model outputs are probabilistic and should be interpreted cautiously in critical systems.
Limitations
Limited to time-series metrics related to server health (e.g., temperature, power, throughput).
Performance may degrade for very sparse or noisy datasets.
Recommendations
Recommendations
Use the model in conjunction with other predictive maintenance tools.
Validate model predictions against domain knowledge to ensure accuracy.
How to Get Started with the Model
The Mistral 7B - Time Series Predictor can process time-series queries such as server health metrics and predict failure probabilities and causes. The following Python script demonstrates how to load the model and generate responses.
Code
from transformers import AutoModelForCausalLM, AutoTokenizer
What is the failure probability and Cause for Server 'x' on Date : [mm/dd/yy]?
Expected Ouptut: The failure probability for ET-1 on 11th July is 0.72. The likely cause is overheating due to sustained high temperatures over the past week.
Requirements
Dependencies:
pip install torch transformers
Training Details
Training Data
Source: Synthetic and real-world server metrics from Esperanto servers.
Dataset: Synthetic data generated with periodic patterns (e.g., cosine functions) combined with operational zones (green, yellow, red).
Training Procedure
Preprocessing [optional]
Numerical to Textual Conversion:
All numerical metrics (e.g., temperature, power consumption, throughput) were converted into descriptive textual data to make it comprehensible for the language model. For example:
Numerical Input: {"temperature": [40, 42, 43]}
Converted Text: "The temperature increased steadily from 40°C to 43°C over the last three readings."
Domain-Specific Context:
Prompts were carefully designed to incorporate domain knowledge, guiding the model to focus on server health indicators and operational risks.
Example prompts include:
"Analyze the following server performance metrics and predict potential failures."
"Based on the provided metrics, forecast failure probabilities and identify potential causes."
These prompts ensured the model understood the critical relationships between input metrics and their operational implications.
Training Hyperparameters
Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
Training time: ~30 hours on NVIDIA A100 GPUs
Model size: ~7B parameters
Evaluation
Testing Data, Factors & Metrics
Testing Data
Validation set: 10% of synthetic and real-world server performance data.
