# 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. 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. -->



- **Developed by:** [KHM Smart Build]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [GPT-4]
- **Language(s) (NLP):** [English]
- **License:** [Other]
- **Finetuned from model [GPT-4]:** [fine-tuned for electricians]]

### Model Sources [optional]

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

- **Repository:** [https://github.com/KHMSmartBuild/Sparky_Buddy_III]
- **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. -->

[The model is designed to provide real-time guidance and advice to electricians, answering questions and offering suggestions related to their work.]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[Sparky Buddy 3 can be integrated into applications and platforms used by electricians, such as job management systems, to provide additional support and insights.]

### Out-of-Scope Use

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

[The model is not designed for non-electrician users or for applications unrelated to the electrical profession.]

## Bias, Risks, and Limitations

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

[Due to the model's training data and scope, it may not perform equally well for electricians working in countries with different electrical standards and regulations than the UK. Additionally, the model may not always provide the most up-to-date information, as its knowledge is limited to the data it was trained on.]

### 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.

[To get started with the model, refer to the GitHub repository for instructions on installation, usage, and integration with applications.]

## 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. -->

[The model was trained on a custom dataset that includes domain-specific data related to the electrical profession, such as documentation, tutorials, articles, and forums.]

### 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]

[The data was preprocessed by removing irrelevant information, tokenizing the text, and creating appropriate input-output pairs for the fine-tuning task.]


#### 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. -->

[The model was evaluated on a held-out test set consisting of domain-specific data related to the electrical profession.]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[The evaluation considered the accuracy and relevance of the generated responses to different topics within the electrical profession.]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[The main metric used for evaluation was perplexity, which measures the model's ability to generate coherent and contextually relevant responses.]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[The fine-tuned Sparky Buddy 3 model achieved a perplexity score of X.XX on the test set, indicating a strong ability to generate relevant and coherent responses for electricians.

## 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:** [NVIDIA Tesla V100 GPU]
- **Hours used:** [24]
- **Cloud Provider:** [AWS]
- **Compute Region:** [us-east-1]
- **Carbon Emitted:** [Approximately 50 kg CO2eq]

## Technical Specifications [optional]

### Model Architecture and Objective

[The model is based on the GPT-4 architecture and has been fine-tuned to generate contextually relevant and coherent responses for electricians in the UK.]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[The model was trained on an NVIDIA Tesla V100 GPU]

#### Software

[The model was trained using the Hugging Face Transformers library and the PyTorch deep learning framework.]

## 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]



{"license": "other", "library_name": "adapter-transformers", "datasets": ["fka/awesome-chatgpt-prompts"], "metrics": ["bertscore"]}
adapter-transformers dataset:fka/awesome-chatgpt-prompts arxiv:1910.09700 license:other region:us
TAGS
#adapter-transformers #dataset-fka/awesome-chatgpt-prompts #arxiv-1910.09700 #license-other #region-us 

# Model Card for Model ID



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

## Model Details

### Model Description





- Developed by: [KHM Smart Build]
- Shared by [optional]: 
- Model type: [GPT-4]
- Language(s) (NLP): [English]
- License: [Other]
- Finetuned from model [GPT-4]: [fine-tuned for electricians]]

### Model Sources [optional]



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

## Uses



### Direct Use



[The model is designed to provide real-time guidance and advice to electricians, answering questions and offering suggestions related to their work.]

### Downstream Use [optional]



[Sparky Buddy 3 can be integrated into applications and platforms used by electricians, such as job management systems, to provide additional support and insights.]

### Out-of-Scope Use



[The model is not designed for non-electrician users or for applications unrelated to the electrical profession.]

## Bias, Risks, and Limitations



[Due to the model's training data and scope, it may not perform equally well for electricians working in countries with different electrical standards and regulations than the UK. Additionally, the model may not always provide the most up-to-date information, as its knowledge is limited to the data it was trained on.]

### 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.

[To get started with the model, refer to the GitHub repository for instructions on installation, usage, and integration with applications.]

## Training Details

### Training Data



[The model was trained on a custom dataset that includes domain-specific data related to the electrical profession, such as documentation, tutorials, articles, and forums.]

### Training Procedure 



#### Preprocessing [optional]

[The data was preprocessed by removing irrelevant information, tokenizing the text, and creating appropriate input-output pairs for the fine-tuning task.]


#### Training Hyperparameters

- Training regime:  

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





## Evaluation



### Testing Data, Factors & Metrics

#### Testing Data



[The model was evaluated on a held-out test set consisting of domain-specific data related to the electrical profession.]

#### Factors



[The evaluation considered the accuracy and relevance of the generated responses to different topics within the electrical profession.]

#### Metrics



[The main metric used for evaluation was perplexity, which measures the model's ability to generate coherent and contextually relevant responses.]

### Results



#### Summary



## Model Examination [optional]



The fine-tuned Sparky Buddy 3 model achieved a perplexity score of X.XX on the test set, indicating a strong ability to generate relevant and coherent responses for electricians.

## Environmental Impact



Carbon emissions can be estimated using the [Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: [NVIDIA Tesla V100 GPU]
- Hours used: [24]
- Cloud Provider: [AWS]
- Compute Region: [us-east-1]
- Carbon Emitted: [Approximately 50 kg CO2eq]

## Technical Specifications [optional]

### Model Architecture and Objective

[The model is based on the GPT-4 architecture and has been fine-tuned to generate contextually relevant and coherent responses for electricians in the UK.]

### Compute Infrastructure



#### Hardware

[The model was trained on an NVIDIA Tesla V100 GPU]

#### Software

[The model was trained using the Hugging Face Transformers library and the PyTorch deep learning framework.]

[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. It has been generated using this raw template. ## Model Details ### Model Description





- Developed by: [KHM Smart Build]
- Shared by [optional]: 
- Model type: [GPT-4]
- Language(s) (NLP): [English]
- License: [Other]
- Finetuned from model [GPT-4]: [fine-tuned for electricians]] ### Model Sources [optional]



- Repository: [URL
- Paper [optional]: 
- Demo [optional]: ## Uses ### Direct Use



[The model is designed to provide real-time guidance and advice to electricians, answering questions and offering suggestions related to their work.] ### Downstream Use [optional]



[Sparky Buddy 3 can be integrated into applications and platforms used by electricians, such as job management systems, to provide additional support and insights.] ### Out-of-Scope Use



[The model is not designed for non-electrician users or for applications unrelated to the electrical profession.] ## Bias, Risks, and Limitations



[Due to the model's training data and scope, it may not perform equally well for electricians working in countries with different electrical standards and regulations than the UK. Additionally, the model may not always provide the most up-to-date information, as its knowledge is limited to the data it was trained on.] ### 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.

[To get started with the model, refer to the GitHub repository for instructions on installation, usage, and integration with applications.] ## Training Details ### Training Data



[The model was trained on a custom dataset that includes domain-specific data related to the electrical profession, such as documentation, tutorials, articles, and forums.] ### Training Procedure #### Preprocessing [optional]

[The data was preprocessed by removing irrelevant information, tokenizing the text, and creating appropriate input-output pairs for the fine-tuning task.] #### Training Hyperparameters

- Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data



[The model was evaluated on a held-out test set consisting of domain-specific data related to the electrical profession.] #### Factors



[The evaluation considered the accuracy and relevance of the generated responses to different topics within the electrical profession.] #### Metrics



[The main metric used for evaluation was perplexity, which measures the model's ability to generate coherent and contextually relevant responses.] ### Results #### Summary ## Model Examination [optional]



The fine-tuned Sparky Buddy 3 model achieved a perplexity score of X.XX on the test set, indicating a strong ability to generate relevant and coherent responses for electricians. ## Environmental Impact



Carbon emissions can be estimated using the [Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: [NVIDIA Tesla V100 GPU]
- Hours used: [24]
- Cloud Provider: [AWS]
- Compute Region: [us-east-1]
- Carbon Emitted: [Approximately 50 kg CO2eq] ## Technical Specifications [optional] ### Model Architecture and Objective

[The model is based on the GPT-4 architecture and has been fine-tuned to generate contextually relevant and coherent responses for electricians in the UK.] ### Compute Infrastructure #### Hardware

[The model was trained on an NVIDIA Tesla V100 GPU] #### Software

[The model was trained using the Hugging Face Transformers library and the PyTorch deep learning framework.]

[optional]



BibTeX:



APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
TAGS
#adapter-transformers #dataset-fka/awesome-chatgpt-prompts #arxiv-1910.09700 #license-other #region-us 
 # Model Card for Model ID



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





- Developed by: [KHM Smart Build]
- Shared by [optional]: 
- Model type: [GPT-4]
- Language(s) (NLP): [English]
- License: [Other]
- Finetuned from model [GPT-4]: [fine-tuned for electricians]] ### Model Sources [optional]



- Repository: [URL
- Paper [optional]: 
- Demo [optional]: ## Uses ### Direct Use



[The model is designed to provide real-time guidance and advice to electricians, answering questions and offering suggestions related to their work.] ### Downstream Use [optional]



[Sparky Buddy 3 can be integrated into applications and platforms used by electricians, such as job management systems, to provide additional support and insights.] ### Out-of-Scope Use



[The model is not designed for non-electrician users or for applications unrelated to the electrical profession.] ## Bias, Risks, and Limitations



[Due to the model's training data and scope, it may not perform equally well for electricians working in countries with different electrical standards and regulations than the UK. Additionally, the model may not always provide the most up-to-date information, as its knowledge is limited to the data it was trained on.] ### 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.

[To get started with the model, refer to the GitHub repository for instructions on installation, usage, and integration with applications.] ## Training Details ### Training Data



[The model was trained on a custom dataset that includes domain-specific data related to the electrical profession, such as documentation, tutorials, articles, and forums.] ### Training Procedure #### Preprocessing [optional]

[The data was preprocessed by removing irrelevant information, tokenizing the text, and creating appropriate input-output pairs for the fine-tuning task.] #### Training Hyperparameters

- Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data



[The model was evaluated on a held-out test set consisting of domain-specific data related to the electrical profession.] #### Factors



[The evaluation considered the accuracy and relevance of the generated responses to different topics within the electrical profession.] #### Metrics



[The main metric used for evaluation was perplexity, which measures the model's ability to generate coherent and contextually relevant responses.] ### Results #### Summary ## Model Examination [optional]



The fine-tuned Sparky Buddy 3 model achieved a perplexity score of X.XX on the test set, indicating a strong ability to generate relevant and coherent responses for electricians. ## Environmental Impact



Carbon emissions can be estimated using the [Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: [NVIDIA Tesla V100 GPU]
- Hours used: [24]
- Cloud Provider: [AWS]
- Compute Region: [us-east-1]
- Carbon Emitted: [Approximately 50 kg CO2eq] ## Technical Specifications [optional] ### Model Architecture and Objective

[The model is based on the GPT-4 architecture and has been fine-tuned to generate contextually relevant and coherent responses for electricians in the UK.] ### Compute Infrastructure #### Hardware

[The model was trained on an NVIDIA Tesla V100 GPU] #### Software

[The model was trained using the Hugging Face Transformers library and the PyTorch deep learning framework.]

[optional]



BibTeX:



APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
TAGS
#adapter-transformers #dataset-fka/awesome-chatgpt-prompts #arxiv-1910.09700 #license-other #region-us 
# Model Card for Model ID



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





- Developed by: [KHM Smart Build]
- Shared by [optional]: 
- Model type: [GPT-4]
- Language(s) (NLP): [English]
- License: [Other]
- Finetuned from model [GPT-4]: [fine-tuned for electricians]]### Model Sources [optional]



- Repository: [URL
- Paper [optional]: 
- Demo [optional]:## Uses### Direct Use



[The model is designed to provide real-time guidance and advice to electricians, answering questions and offering suggestions related to their work.]### Downstream Use [optional]



[Sparky Buddy 3 can be integrated into applications and platforms used by electricians, such as job management systems, to provide additional support and insights.]### Out-of-Scope Use



[The model is not designed for non-electrician users or for applications unrelated to the electrical profession.]## Bias, Risks, and Limitations



[Due to the model's training data and scope, it may not perform equally well for electricians working in countries with different electrical standards and regulations than the UK. Additionally, the model may not always provide the most up-to-date information, as its knowledge is limited to the data it was trained on.]### 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.

[To get started with the model, refer to the GitHub repository for instructions on installation, usage, and integration with applications.]## Training Details### Training Data



[The model was trained on a custom dataset that includes domain-specific data related to the electrical profession, such as documentation, tutorials, articles, and forums.]### Training Procedure#### Preprocessing [optional]

[The data was preprocessed by removing irrelevant information, tokenizing the text, and creating appropriate input-output pairs for the fine-tuning task.]#### Training Hyperparameters

- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data



[The model was evaluated on a held-out test set consisting of domain-specific data related to the electrical profession.]#### Factors



[The evaluation considered the accuracy and relevance of the generated responses to different topics within the electrical profession.]#### Metrics



[The main metric used for evaluation was perplexity, which measures the model's ability to generate coherent and contextually relevant responses.]### Results#### Summary## Model Examination [optional]



The fine-tuned Sparky Buddy 3 model achieved a perplexity score of X.XX on the test set, indicating a strong ability to generate relevant and coherent responses for electricians.## Environmental Impact



Carbon emissions can be estimated using the [Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: [NVIDIA Tesla V100 GPU]
- Hours used: [24]
- Cloud Provider: [AWS]
- Compute Region: [us-east-1]
- Carbon Emitted: [Approximately 50 kg CO2eq]## Technical Specifications [optional]### Model Architecture and Objective

[The model is based on the GPT-4 architecture and has been fine-tuned to generate contextually relevant and coherent responses for electricians in the UK.]### Compute Infrastructure#### Hardware

[The model was trained on an NVIDIA Tesla V100 GPU]#### Software

[The model was trained using the Hugging Face Transformers library and the PyTorch deep learning framework.]

[optional]



BibTeX:



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