lmma25
/

log-gen

@@ -25,20 +25,17 @@ This is a Llama 3 model finetuned on execution logs to be used for a sock-shop a
 This model was finetuned on a variety of system logs of a sock shop app. Given a log chunk of 10 messages, it generates the next log message according to normal execution.
 - **Developed by:** Luís Almeida, Diego Pedroso, Lucas Pulcinelli, William Aisawa, Sarita Bruschi, Inês Dutra
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
 - **Language(s) (NLP):** [English]
 - **License:** [llama3]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
 - **Repository:** https://github.com/lasdpc-icmc/maia
 - **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## Uses
@@ -50,14 +47,6 @@ finetuned on a sample of execution logs of the new system.
 Direct plugin to the sock-shop app.
-[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 -->
-[More Information Needed]
 ### Out-of-Scope Use
 The usage of this model on execution logs that it hasn't been finetuned on may yield bad results.
@@ -77,28 +66,18 @@ anomalies.
 ### 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. -->
-[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. -->
-#### Preprocessing [optional]
-[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 -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
 ## Evaluation
@@ -106,67 +85,21 @@ anomalies.
 ### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[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 -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **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]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
 ## Citation [optional]
@@ -174,26 +107,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 **BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
 [More Information Needed]

 This model was finetuned on a variety of system logs of a sock shop app. Given a log chunk of 10 messages, it generates the next log message according to normal execution.
 - **Developed by:** Luís Almeida, Diego Pedroso, Lucas Pulcinelli, William Aisawa, Sarita Bruschi, Inês Dutra
+- **Model type:** Text Generation
 - **Language(s) (NLP):** [English]
 - **License:** [llama3]
+- **Finetuned from model:** Llama 3 8b
+### Model Sources
 <!-- Provide the basic links for the model. -->
 - **Repository:** https://github.com/lasdpc-icmc/maia
 - **Paper [optional]:** [More Information Needed]
 ## Uses
 Direct plugin to the sock-shop app.
 ### Out-of-Scope Use
 The usage of this model on execution logs that it hasn't been finetuned on may yield bad results.
 ### Training Data
+https://huggingface.co/datasets/lmma25/sock-shop-logs-train
 ### Training Procedure
+The model was finetuned using the SFTTrainer from the transformer's library in an autoregressive way.
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 #### Training Hyperparameters
+- **Training regime:** 10 epochs, AdamW optimizer, 1e-4 learning rate, bf16, weight decay 0.01, max gradient norm 0.3, cosine learning rate scheduler
 ## Evaluation
 ### Testing Data, Factors & Metrics
+#### Testing Data
+https://huggingface.co/datasets/lmma25/sock-shop-logs-test
 #### Metrics
+The model was used to detect anomalies on a small sample of execution logs, achieving a precision of 0.77 and a recall of 1. Precision and recall metrics were used since
+they allow for the accurate assessment of model behavior in regards to false positives and false negatives.
 ### Results
+Precision 0.77
+Recall 1
 ## Citation [optional]
 **BibTeX:**
 [More Information Needed]