Model Card for Model ID
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
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- 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
- model_name = "Esperanto/Mistral-7B-TimeSeriesReasoner"
- tokenizer = AutoTokenizer.from_pretrained(model_name)
- model = AutoModelForCausalLM.from_pretrained(model_name)
prompt = "What is the failure probability and Cause for Server 'x' on Date : [mm/dd/yy]?"
- input_ids = tokenizer(prompt, return_tensors='pt')['input_ids']
- output = model.generate(input_ids=input_ids, max_new_tokens=100)
- response = tokenizer.decode(output[0])
- print(response)
Example Prompt
- 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.
Factors
Model evaluated for:
- Failure prediction accuracy with cause.
Results
Metrics
Results
Summary
Model Examination [optional]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Hardware
Runs on both GPU A100 and Esperanto ET-SoC
Software
Use Pytorch, Huggingface transformers library
Citation [optional]
Esperanto Blog :
Model Card Authors [optional]
Sivakrishna Yaganti and Shankar Jayaratnam
Model Card Contact
- Downloads last month
- 0
Model tree for Esperanto/Mistral-7B-TimeSeriesReasoner
Base model
mistralai/Mistral-7B-v0.3