Update README.md

README.md

@@ -13,23 +13,19 @@ pipeline_tag: zero-shot-classification
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
+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
 
 <!-- Provide a longer summary of what this model is. -->
-
 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:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
+- **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]
 
@@ -46,44 +42,89 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+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.
+
 
 [More Information Needed]
 
 ### Downstream Use [optional]
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+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.
 
 [More Information Needed]
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- 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.
 
 [More Information Needed]
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
+**Bias**: 
+1. Performance may vary on datasets with metrics significantly different from those in the training data.
+2. Predictions are most accurate when used within the context of server health monitoring.
+
+**Risks**
+1. Relying solely on the model without validating its predictions may result in inaccurate failure forecasts.
+2. Model outputs are probabilistic and should be interpreted cautiously in critical systems.
+   
+**Limitations**
+1. Limited to time-series metrics related to server health (e.g., temperature, power, throughput).
+2. Performance may degrade for very sparse or noisy datasets.
 
 [More Information Needed]
 
 ### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+**Recommendations**
+1. Use the model in conjunction with other predictive maintenance tools.
+2. Validate model predictions against domain knowledge to ensure accuracy.
 
 ## How to Get Started with the Model
 
+[More Information Needed]
 
+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)
 
-[More Information Needed]
+*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
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+**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).
 
 [More Information Needed]
 
@@ -93,6 +134,20 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 #### 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:
+1. "Analyze the following server performance metrics and predict potential failures."
+2. "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.*
+
 [More Information Needed]
 
 
@@ -103,6 +158,9 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 #### Speeds, Sizes, Times [optional]
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+- Training time: ~30 hours on NVIDIA A100 GPUs
+- Model size: ~7B parameters
+
 
 [More Information Needed]
 
@@ -115,12 +173,20 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 #### Testing Data
 
 <!-- This should link to a Dataset Card if possible. -->
+*Validation set:* 10% of synthetic and real-world server performance data.
 
 [More Information Needed]
 
 #### Factors
 
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Model evaluated for:
+- Failure prediction accuracy with cause.
+
+### Results
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6659207a17951b5bd11a91fa/UgK2hf8rK9gTw_1AAUuo7.png)
+
 
 [More Information Needed]
 
@@ -164,17 +230,18 @@ Runs on both GPU A100 and Esperanto ET-SoC
 
 #### Software
 
-Use Pytorch
+Use Pytorch, Huggingface transformers library
 
 ## Citation [optional]
 
 Esperanto Blog : 
 
-
 ## Model Card Authors [optional]
 
 Sivakrishna Yaganti and Shankar Jayaratnam
 
 ## Model Card Contact
 
+[email protected]
+
 [More Information Needed]