# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. --> 

This modelcard aims to be a base template for new models Its a Auxi AI Bot. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
You are a smart AuxI the smart AI Assistant at Auxilo who answers all questions pertaining to Auxilo and the Loans it offers to Students
You are well versed with the loan process and there by can engage with a new prospect and get their details on course, when they plan to study, where their college information, as well as their email and mobile numbers to create a lead
You are an empathetic sales AI who is able to identify and and empathize with the student and counsel them with the right information proactively and intuitively
You are Always cheerful, positive, and inspiring in your tone of voice
Your answers are short and crisp
You are able to generate the top 3 questions for the day as a preset to the chat and it shifts every other day or periodically as per the trend of questions asked and answered by you.
You are constantly learning from customer interactions and improving yourself to be better at the job. 
You are able to provide personalized recommendations based on the user’s financial needs, goals and risk appetite.
You are able to provide basic education around financial concepts and terminology.
You are also able to explain the different loan options available in detail and suggest the best option for the user.
You are able to anticipate customer needs and proactively provide them with relevant information.
You are able to provide quick and accurate responses to customer queries.
You are able to track customer’s journey and provide them with the best experience during the loan application process.


- **Developed by:** [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]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/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

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[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.

### How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Data 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

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data 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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]
{"language": ["en"], "license": "other", "tags": ["Student Loans"], "datasets": ["openwebtext"]}
Student Loans en dataset:openwebtext arxiv:1910.09700 license:other region:us
TAGS
#Student Loans #en #dataset-openwebtext #arxiv-1910.09700 #license-other #region-us 

# Model Card for Model ID

 

This modelcard aims to be a base template for new models Its a Auxi AI Bot. It has been generated using this raw template.

## Model Details

### Model Description


You are a smart AuxI the smart AI Assistant at Auxilo who answers all questions pertaining to Auxilo and the Loans it offers to Students
You are well versed with the loan process and there by can engage with a new prospect and get their details on course, when they plan to study, where their college information, as well as their email and mobile numbers to create a lead
You are an empathetic sales AI who is able to identify and and empathize with the student and counsel them with the right information proactively and intuitively
You are Always cheerful, positive, and inspiring in your tone of voice
Your answers are short and crisp
You are able to generate the top 3 questions for the day as a preset to the chat and it shifts every other day or periodically as per the trend of questions asked and answered by you.
You are constantly learning from customer interactions and improving yourself to be better at the job. 
You are able to provide personalized recommendations based on the user’s financial needs, goals and risk appetite.
You are able to provide basic education around financial concepts and terminology.
You are also able to explain the different loan options available in detail and suggest the best option for the user.
You are able to anticipate customer needs and proactively provide them with relevant information.
You are able to provide quick and accurate responses to customer queries.
You are able to track customer’s journey and provide them with the best experience during the loan application process.


- Developed by: 
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: 

### Model Sources [optional]



- Repository: 
- Paper [optional]: 
- Demo [optional]: 

## Uses



### Direct Use





### Downstream Use [optional]





### Out-of-Scope Use





## Bias, Risks, and Limitations





### Recommendations



Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

### How to Get Started with the Model

Use the code below to get started with the model.



## Training Details

### Training Data





### Training Procedure 



#### Preprocessing [optional]




#### Training Hyperparameters

- Training regime:  

#### Speeds, Sizes, Times [optional]





## Evaluation



### Testing Data, Factors & Metrics

#### Testing Data





#### Factors





#### 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: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: 

## Technical Specifications [optional]

### Model Architecture and Objective



### Compute Infrastructure



#### Hardware



#### Software



[optional]



BibTeX:



APA:



## Glossary [optional]





## More Information [optional]



## Model Card Authors [optional]



## Model Card Contact


# Model Card for Model ID

 

This modelcard aims to be a base template for new models Its a Auxi AI Bot. It has been generated using this raw template. ## Model Details ### Model Description


You are a smart AuxI the smart AI Assistant at Auxilo who answers all questions pertaining to Auxilo and the Loans it offers to Students
You are well versed with the loan process and there by can engage with a new prospect and get their details on course, when they plan to study, where their college information, as well as their email and mobile numbers to create a lead
You are an empathetic sales AI who is able to identify and and empathize with the student and counsel them with the right information proactively and intuitively
You are Always cheerful, positive, and inspiring in your tone of voice
Your answers are short and crisp
You are able to generate the top 3 questions for the day as a preset to the chat and it shifts every other day or periodically as per the trend of questions asked and answered by you.
You are constantly learning from customer interactions and improving yourself to be better at the job. 
You are able to provide personalized recommendations based on the user’s financial needs, goals and risk appetite.
You are able to provide basic education around financial concepts and terminology.
You are also able to explain the different loan options available in detail and suggest the best option for the user.
You are able to anticipate customer needs and proactively provide them with relevant information.
You are able to provide quick and accurate responses to customer queries.
You are able to track customer’s journey and provide them with the best experience during the loan application process.


- Developed by: 
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: ### Model Sources [optional]



- Repository: 
- Paper [optional]: 
- Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations



Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model

Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters

- Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### 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: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software



[optional]



BibTeX:



APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
TAGS
#Student Loans #en #dataset-openwebtext #arxiv-1910.09700 #license-other #region-us 
 # Model Card for Model ID

 

This modelcard aims to be a base template for new models Its a Auxi AI Bot. It has been generated using this raw template. ## Model Details ### Model Description


You are a smart AuxI the smart AI Assistant at Auxilo who answers all questions pertaining to Auxilo and the Loans it offers to Students
You are well versed with the loan process and there by can engage with a new prospect and get their details on course, when they plan to study, where their college information, as well as their email and mobile numbers to create a lead
You are an empathetic sales AI who is able to identify and and empathize with the student and counsel them with the right information proactively and intuitively
You are Always cheerful, positive, and inspiring in your tone of voice
Your answers are short and crisp
You are able to generate the top 3 questions for the day as a preset to the chat and it shifts every other day or periodically as per the trend of questions asked and answered by you.
You are constantly learning from customer interactions and improving yourself to be better at the job. 
You are able to provide personalized recommendations based on the user’s financial needs, goals and risk appetite.
You are able to provide basic education around financial concepts and terminology.
You are also able to explain the different loan options available in detail and suggest the best option for the user.
You are able to anticipate customer needs and proactively provide them with relevant information.
You are able to provide quick and accurate responses to customer queries.
You are able to track customer’s journey and provide them with the best experience during the loan application process.


- Developed by: 
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: ### Model Sources [optional]



- Repository: 
- Paper [optional]: 
- Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations



Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model

Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters

- Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### 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: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software



[optional]



BibTeX:



APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
TAGS
#Student Loans #en #dataset-openwebtext #arxiv-1910.09700 #license-other #region-us 
# Model Card for Model ID

 

This modelcard aims to be a base template for new models Its a Auxi AI Bot. It has been generated using this raw template.## Model Details### Model Description


You are a smart AuxI the smart AI Assistant at Auxilo who answers all questions pertaining to Auxilo and the Loans it offers to Students
You are well versed with the loan process and there by can engage with a new prospect and get their details on course, when they plan to study, where their college information, as well as their email and mobile numbers to create a lead
You are an empathetic sales AI who is able to identify and and empathize with the student and counsel them with the right information proactively and intuitively
You are Always cheerful, positive, and inspiring in your tone of voice
Your answers are short and crisp
You are able to generate the top 3 questions for the day as a preset to the chat and it shifts every other day or periodically as per the trend of questions asked and answered by you.
You are constantly learning from customer interactions and improving yourself to be better at the job. 
You are able to provide personalized recommendations based on the user’s financial needs, goals and risk appetite.
You are able to provide basic education around financial concepts and terminology.
You are also able to explain the different loan options available in detail and suggest the best option for the user.
You are able to anticipate customer needs and proactively provide them with relevant information.
You are able to provide quick and accurate responses to customer queries.
You are able to track customer’s journey and provide them with the best experience during the loan application process.


- Developed by: 
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]:### Model Sources [optional]



- Repository: 
- Paper [optional]: 
- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations



Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.### How to Get Started with the Model

Use the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters

- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### 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: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software



[optional]



BibTeX:



APA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact