

# 🚀 agrimi-7b-lora

<!-- Provide a quick summary of what the model is/does. [Optional] -->
agrimi-7b-lora is a chatbot-like model for dialogue generation. It was built by fine-tuning falcon-7B on the Greek translation of Alpaca dataset. This repo only includes the LoRA adapters from fine-tuning with 🤗&#39;s peft package.

Since, Greek language is not included in the pretrained falcon-7b model the performance of this model is not very good. The purpose of this model is mostly to demonstrate that even using a pretrained model without any knowledge of Greek language it is possible to utilize the global knowledge and apply transfer learning!


# Model Details

## Model Description

<!-- Provide a longer summary of what this model is/does. -->
agrimi-7b-lora is a chatbot-like model for dialogue generation. It was built by fine-tuning falcon-7B on the Greek translation of Alpaca dataset. This repo only includes the LoRA adapters from fine-tuning with 🤗&#39;s peft package.

Since, Greek language is not included in the pretrained falcon-7b model the performance of this model is not very good. The purpose of this model is mostly to demonstrate that even using a pretrained model without any knowledge of Greek language it is possible to utilize the global knowledge and apply transfer learning!

- **Developed by:** More information needed
- **Shared by [Optional]:** More information needed
- **Model type:** Language model
- **Language(s) (NLP):** el
- **License:** apache-2.0
- **Parent Model:** More information needed
- **Resources for more information:** More information needed


#  Table of Contents

- [Model Card for agrimi-7b-lora](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Table of Contents](#table-of-contents-1)
- [Model Details](#model-details)
  - [Model Description](#model-description)
- [Uses](#uses)
  - [Direct Use](#direct-use)
  - [Downstream Use [Optional]](#downstream-use-optional)
  - [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
  - [Recommendations](#recommendations)
- [Training Details](#training-details)
  - [Training Data](#training-data)
  - [Training Procedure](#training-procedure)
    - [Preprocessing](#preprocessing)
    - [Speeds, Sizes, Times](#speeds-sizes-times)
- [Evaluation](#evaluation)
  - [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
    - [Testing Data](#testing-data)
    - [Factors](#factors)
    - [Metrics](#metrics)
  - [Results](#results)
- [Model Examination](#model-examination)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications [optional]](#technical-specifications-optional)
  - [Model Architecture and Objective](#model-architecture-and-objective)
  - [Compute Infrastructure](#compute-infrastructure)
    - [Hardware](#hardware)
    - [Software](#software)
- [Citation](#citation)
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- [Model Card Contact](#model-card-contact)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)


# 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. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info 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 -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
 



## Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->




# Bias, Risks, and Limitations

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

Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.


## Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->





# 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 on training data 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

More information needed

### Speeds, Sizes, Times

<!-- 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

# Model Examination

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

<!-- 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]

<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->

Andreas Loupasakis

# Model Card Contact

More information needed

# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>

More information needed

</details>

{"language": ["el"], "license": "apache-2.0", "pipeline_tag": "text-generation"}
text-generation el arxiv:1910.09700 license:apache-2.0 region:us
TAGS
#text-generation #el #arxiv-1910.09700 #license-apache-2.0 #region-us 



#  agrimi-7b-lora


agrimi-7b-lora is a chatbot-like model for dialogue generation. It was built by fine-tuning falcon-7B on the Greek translation of Alpaca dataset. This repo only includes the LoRA adapters from fine-tuning with &#39;s peft package.

Since, Greek language is not included in the pretrained falcon-7b model the performance of this model is not very good. The purpose of this model is mostly to demonstrate that even using a pretrained model without any knowledge of Greek language it is possible to utilize the global knowledge and apply transfer learning!


# Model Details

## Model Description


agrimi-7b-lora is a chatbot-like model for dialogue generation. It was built by fine-tuning falcon-7B on the Greek translation of Alpaca dataset. This repo only includes the LoRA adapters from fine-tuning with &#39;s peft package.

Since, Greek language is not included in the pretrained falcon-7b model the performance of this model is not very good. The purpose of this model is mostly to demonstrate that even using a pretrained model without any knowledge of Greek language it is possible to utilize the global knowledge and apply transfer learning!

- Developed by: More information needed
- Shared by [Optional]: More information needed
- Model type: Language model
- Language(s) (NLP): el
- License: apache-2.0
- Parent Model: More information needed
- Resources for more information: More information needed


#  Table of Contents

- Model Card for agrimi-7b-lora
- Table of Contents
- Table of Contents
- Model Details
  - Model Description
- Uses
  - Direct Use
  - [Downstream Use [Optional]](#downstream-use-optional)
  - Out-of-Scope Use
- Bias, Risks, and Limitations
  - Recommendations
- Training Details
  - Training Data
  - Training Procedure
    - Preprocessing
    - Speeds, Sizes, Times
- Evaluation
  - Testing Data, Factors & Metrics
    - Testing Data
    - Factors
    - Metrics
  - Results
- Model Examination
- Environmental Impact
- [Technical Specifications [optional]](#technical-specifications-optional)
  - Model Architecture and Objective
  - Compute Infrastructure
    - Hardware
    - Software
- Citation
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- Model Card Contact
- How to Get Started with the Model


# Uses



## Direct Use







## Downstream Use [Optional]



 



## Out-of-Scope Use







# Bias, Risks, and Limitations



Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.


## Recommendations







# Training Details

## Training Data



More information on training data needed


## Training Procedure



### Preprocessing

More information needed

### Speeds, Sizes, Times



More information needed
 
# Evaluation



## Testing Data, Factors & Metrics

### Testing Data



More information needed


### Factors



More information needed

### Metrics



More information needed

## Results 

More information needed

# Model Examination

More information needed

# 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]

## Model Architecture and Objective

More information needed

## Compute Infrastructure

More information needed

### Hardware

More information needed

### Software

More information needed

BibTeX:

More information needed

APA:

More information needed

# Glossary [optional]



More information needed

# More Information [optional]

More information needed

# Model Card Authors [optional]



Andreas Loupasakis

# Model Card Contact

More information needed

# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>

More information needed

</details>

#  agrimi-7b-lora


agrimi-7b-lora is a chatbot-like model for dialogue generation. It was built by fine-tuning falcon-7B on the Greek translation of Alpaca dataset. This repo only includes the LoRA adapters from fine-tuning with &#39;s peft package.

Since, Greek language is not included in the pretrained falcon-7b model the performance of this model is not very good. The purpose of this model is mostly to demonstrate that even using a pretrained model without any knowledge of Greek language it is possible to utilize the global knowledge and apply transfer learning! # Model Details ## Model Description


agrimi-7b-lora is a chatbot-like model for dialogue generation. It was built by fine-tuning falcon-7B on the Greek translation of Alpaca dataset. This repo only includes the LoRA adapters from fine-tuning with &#39;s peft package.

Since, Greek language is not included in the pretrained falcon-7b model the performance of this model is not very good. The purpose of this model is mostly to demonstrate that even using a pretrained model without any knowledge of Greek language it is possible to utilize the global knowledge and apply transfer learning!

- Developed by: More information needed
- Shared by [Optional]: More information needed
- Model type: Language model
- Language(s) (NLP): el
- License: apache-2.0
- Parent Model: More information needed
- Resources for more information: More information needed #  Table of Contents

- Model Card for agrimi-7b-lora
- Table of Contents
- Table of Contents
- Model Details
  - Model Description
- Uses
  - Direct Use
  - [Downstream Use [Optional]](#downstream-use-optional)
  - Out-of-Scope Use
- Bias, Risks, and Limitations
  - Recommendations
- Training Details
  - Training Data
  - Training Procedure
    - Preprocessing
    - Speeds, Sizes, Times
- Evaluation
  - Testing Data, Factors & Metrics
    - Testing Data
    - Factors
    - Metrics
  - Results
- Model Examination
- Environmental Impact
- [Technical Specifications [optional]](#technical-specifications-optional)
  - Model Architecture and Objective
  - Compute Infrastructure
    - Hardware
    - Software
- Citation
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- Model Card Contact
- How to Get Started with the Model # Uses ## Direct Use ## Downstream Use [Optional] ## Out-of-Scope Use # Bias, Risks, and Limitations



Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations # Training Details ## Training Data



More information on training data needed ## Training Procedure ### Preprocessing

More information needed ### Speeds, Sizes, Times



More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data



More information needed ### Factors



More information needed ### Metrics



More information needed ## Results 

More information needed # Model Examination

More information needed # 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] ## Model Architecture and Objective

More information needed ## Compute Infrastructure

More information needed ### Hardware

More information needed ### Software

More information needed

BibTeX:

More information needed

APA:

More information needed # Glossary [optional]



More information needed # More Information [optional]

More information needed # Model Card Authors [optional]



Andreas Loupasakis # Model Card Contact

More information needed # How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>

More information needed

</details>
TAGS
#text-generation #el #arxiv-1910.09700 #license-apache-2.0 #region-us 
 #  agrimi-7b-lora


agrimi-7b-lora is a chatbot-like model for dialogue generation. It was built by fine-tuning falcon-7B on the Greek translation of Alpaca dataset. This repo only includes the LoRA adapters from fine-tuning with &#39;s peft package.

Since, Greek language is not included in the pretrained falcon-7b model the performance of this model is not very good. The purpose of this model is mostly to demonstrate that even using a pretrained model without any knowledge of Greek language it is possible to utilize the global knowledge and apply transfer learning! # Model Details ## Model Description


agrimi-7b-lora is a chatbot-like model for dialogue generation. It was built by fine-tuning falcon-7B on the Greek translation of Alpaca dataset. This repo only includes the LoRA adapters from fine-tuning with &#39;s peft package.

Since, Greek language is not included in the pretrained falcon-7b model the performance of this model is not very good. The purpose of this model is mostly to demonstrate that even using a pretrained model without any knowledge of Greek language it is possible to utilize the global knowledge and apply transfer learning!

- Developed by: More information needed
- Shared by [Optional]: More information needed
- Model type: Language model
- Language(s) (NLP): el
- License: apache-2.0
- Parent Model: More information needed
- Resources for more information: More information needed #  Table of Contents

- Model Card for agrimi-7b-lora
- Table of Contents
- Table of Contents
- Model Details
  - Model Description
- Uses
  - Direct Use
  - [Downstream Use [Optional]](#downstream-use-optional)
  - Out-of-Scope Use
- Bias, Risks, and Limitations
  - Recommendations
- Training Details
  - Training Data
  - Training Procedure
    - Preprocessing
    - Speeds, Sizes, Times
- Evaluation
  - Testing Data, Factors & Metrics
    - Testing Data
    - Factors
    - Metrics
  - Results
- Model Examination
- Environmental Impact
- [Technical Specifications [optional]](#technical-specifications-optional)
  - Model Architecture and Objective
  - Compute Infrastructure
    - Hardware
    - Software
- Citation
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- Model Card Contact
- How to Get Started with the Model # Uses ## Direct Use ## Downstream Use [Optional] ## Out-of-Scope Use # Bias, Risks, and Limitations



Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations # Training Details ## Training Data



More information on training data needed ## Training Procedure ### Preprocessing

More information needed ### Speeds, Sizes, Times



More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data



More information needed ### Factors



More information needed ### Metrics



More information needed ## Results 

More information needed # Model Examination

More information needed # 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] ## Model Architecture and Objective

More information needed ## Compute Infrastructure

More information needed ### Hardware

More information needed ### Software

More information needed

BibTeX:

More information needed

APA:

More information needed # Glossary [optional]



More information needed # More Information [optional]

More information needed # Model Card Authors [optional]



Andreas Loupasakis # Model Card Contact

More information needed # How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>

More information needed

</details>