Esperanto
/

Mistral-7B-TimeSeriesReasoner

@@ -47,9 +47,6 @@ 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 -->
@@ -57,16 +54,12 @@ 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. -->
@@ -82,8 +75,6 @@ The model is ideal for integration into platforms such as Splunk and Grafana to:
 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. -->
@@ -93,8 +84,6 @@ The model is ideal for integration into platforms such as Splunk and Grafana to:
 ## 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
@@ -109,7 +98,6 @@ The Mistral 7B - Time Series Predictor can process time-series queries such as s
 - 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.
@@ -126,8 +114,6 @@ The Mistral 7B - Time Series Predictor can process time-series queries such as s
 **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]
 ### Training Procedure
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
@@ -148,9 +134,6 @@ Prompts were carefully designed to incorporate domain knowledge, guiding the mod
 *These prompts ensured the model understood the critical relationships between input metrics and their operational implications.*
-[More Information Needed]
 #### Training Hyperparameters
 - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
@@ -161,9 +144,6 @@ Prompts were carefully designed to incorporate domain knowledge, guiding the mod
 - Training time: ~30 hours on NVIDIA A100 GPUs
 - Model size: ~7B parameters
-[More Information Needed]
 ## Evaluation
 <!-- This section describes the evaluation protocols and provides the results. -->
@@ -175,8 +155,6 @@ Prompts were carefully designed to incorporate domain knowledge, guiding the mod
 <!-- 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. -->
@@ -187,29 +165,20 @@ Model evaluated for:
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6659207a17951b5bd11a91fa/UgK2hf8rK9gTw_1AAUuo7.png)
-[More Information Needed]
 #### Metrics
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
 ### Results
-[More Information Needed]
 #### Summary
 ## Model Examination [optional]
 <!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
 ## Environmental Impact
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
@@ -242,6 +211,4 @@ Sivakrishna Yaganti and Shankar Jayaratnam
 ## Model Card Contact
-shankar.jayaratnam@esperantotech.com
-[More Information Needed]

 - 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]
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 - Monitor server health in real-time.
 - Support decision-making in preventive maintenance.
 ### 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.
 ## Bias, Risks, and Limitations
 <!-- This section is meant to convey both technical and sociotechnical 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.
 ### Recommendations
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 ## 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
 - 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.
 **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
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 *These prompts ensured the model understood the critical relationships between input metrics and their operational implications.*
 #### Training Hyperparameters
 - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 - Training time: ~30 hours on NVIDIA A100 GPUs
 - Model size: ~7B parameters
 ## Evaluation
 <!-- This section describes the evaluation protocols and provides the results. -->
 <!-- This should link to a Dataset Card if possible. -->
 *Validation set:* 10% of synthetic and real-world server performance data.
 #### Factors
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6659207a17951b5bd11a91fa/UgK2hf8rK9gTw_1AAUuo7.png)
 #### Metrics
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 ### Results
 #### Summary
 ## Model Examination [optional]
 <!-- Relevant interpretability work for the model goes here -->
 ## Environmental Impact
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 ## Model Card Contact
+shankar.jayaratnam@esperantotech.com