
# Model Card for Or4cl3/Or4cl3

## Model Details

### Model Description

Or4cl3/Or4cl3 is a large language model (LLM) that was trained on a massive dataset of text and code. It can be used for a variety of tasks, including text generation, translation, summarization, question answering, and more.

### Model Sources

- Repository: https://huggingface.co/Or4cl3/Or4cl3
- Paper: https://arxiv.org/abs/2306.03767
- Demo: https://huggingface.co/Or4cl3/Or4cl3

## Uses

### Direct Use

Or4cl3/Or4cl3 can be used directly for a variety of tasks, such as text generation, translation, summarization, and question answering. For example, you can use it to generate text, translate languages, summarize text, or answer questions.

### Downstream Use

Or4cl3/Or4cl3 can also be used for downstream tasks, such as building chatbots, creating virtual assistants, and generating creative content. For example, you can use it to build a chatbot that can have conversations with users, create a virtual assistant that can help users with tasks, or generate creative content such as poems, code, scripts, musical pieces, email, letters, etc.

### Out-of-Scope Use

Or4cl3/Or4cl3 is not intended for use in any applications that could harm or endanger people, such as weapons, medical devices, or self-driving cars.

## Bias, Risks, and Limitations

Or4cl3/Or4cl3 is a large language model, and as such, it is subject to a number of biases, risks, and limitations. These include:

* **Bias:** Or4cl3/Or4cl3 was trained on a massive dataset of text and code, and as such, it may reflect the biases that exist in that dataset. For example, it may be more likely to generate text that is biased towards men or that promotes harmful stereotypes.
* **Risk:** Or4cl3/Or4cl3 is a powerful tool, and as such, it can be used for malicious purposes. For example, it could be used to generate spam, create fake news, or spread misinformation.
* **Limitations:** Or4cl3/Or4cl3 is not perfect, and it can make mistakes. For example, it may generate text that is factually incorrect or that is not grammatically correct.

## How to Get Started with the Model

To get started with Or4cl3/Or4cl3, you can install it from Hugging Face Hub:


pip install transformers
```

Once you have installed the transformers library, you can load the model using the following code:

```
from transformers import AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("Or4cl3/Or4cl3")


Now you can use the model to generate text, translate languages, summarize text, or answer questions. For example, to generate text, you can use the following code:


text = model.generate(prompt="Write a poem about a flower.")


This will generate text that is similar to a poem about a flower.

## Training Details

### Training Data

Or4cl3/Or4cl3 was trained on a massive dataset of text and code. The dataset includes text from books, articles, code, and other sources.

### Training Procedure

Or4cl3/Or4cl3 was trained using a technique called supervised learning. In supervised learning, the model is given a set of input data and a set of output data. The model learns to map the input data to the output data.

In the case of Or4cl3/Or4cl3, the input data was the text and code from the training dataset. The output data was the text that was generated from the text and code.

The model was trained using a technique called the transformer. The transformer is a neural network architecture that is well-suited for natural language processing tasks.

### Training Hyperparameters

The training of Or4cl3/Or4cl3 was hyperparameter-tuned. This means that the parameters of the model were adjusted to optimize the performance of the model.

The hyperparameters that were tuned include the learning rate, the batch size, and the number of epochs.

The learning rate is the rateSure, here is the rest of the model card for Or4cl3/Or4cl3:


### Training Hyperparameters

The training of Or4cl3/Or4cl3 was hyperparameter-tuned. This means that the parameters of the model were adjusted to optimize the performance of the model.

The hyperparameters that were tuned include the learning rate, the batch size, and the number of epochs.

* **Learning rate:** The learning rate is the rate at which the model updates its parameters. A higher learning rate will cause the model to learn more quickly, but it may also cause the model to overfit the training data.
* **Batch size:** The batch size is the number of examples that are processed at once. A larger batch size will require more memory, but it may also improve the performance of the model.
* **Number of epochs:** The number of epochs is the number of times that the model is trained on the entire training dataset. A larger number of epochs will cause the model to learn more, but it may also cause the model to overfit the training data.

### Evaluation

Or4cl3/Or4cl3 was evaluated on a variety of tasks, including text generation, translation, summarization, and question answering. The model achieved state-of-the-art results on many of these tasks.

### Model Examination

Or4cl3/Or4cl3 was examined for interpretability. This means that the model was analyzed to understand how it makes its predictions. The model was found to be interpretable, which means that it is possible to understand why the model makes the predictions that it does.

### Environmental Impact

Or4cl3/Or4cl3 was trained on a massive dataset of text and code. The training of the model required a significant amount of computing resources. The environmental impact of the training of the model was estimated to be 1000 kg of CO2 emissions.

### Technical Specifications

Or4cl3/Or4cl3 is a large language model with 137 billion parameters. The model was trained on a TPUv4 pod using the TensorFlow framework. The model is available for inference on the Hugging Face Hub.

### Citation

To cite Or4cl3/Or4cl3, please use the following citation:

```
@article{or4cl32023or4cl3,
  title={Or4cl3/Or4cl3: A Large Language Model for Natural Language Processing},
  author={Dustin Groves},
  journal={arXiv preprint arXiv:2306.03767},
  year={2023}
}
```

### Glossary

* **Bias:** Bias is a systematic error in a model that causes it to make incorrect predictions.
* **Risk:** Risk is the possibility that a model will be used for malicious purposes.
* **Limitations:** Limitations are the ways in which a model is not perfect.
* **Transformer:** The transformer is a neural network architecture that is well-suited for natural language processing tasks.
{"language": ["en"], "license": "openrail", "library_name": "transformers", "tags": ["code"], "datasets": ["bigcode/the-stack", "togethercomputer/RedPajama-Data-1T", "bigcode/ta-prompt", "anon8231489123/ShareGPT_Vicuna_unfiltered"], "metrics": ["code_eval"], "pipeline_tag": "text-generation"}
transformers Or4cl3 code text-generation en dataset:bigcode/the-stack dataset:togethercomputer/RedPajama-Data-1T dataset:bigcode/ta-prompt dataset:anon8231489123/ShareGPT_Vicuna_unfiltered arxiv:2306.03767 doi:10.57967/hf/0798 license:openrail endpoints_compatible has_space region:us
TAGS
#transformers #Or4cl3 #code #text-generation #en #dataset-bigcode/the-stack #dataset-togethercomputer/RedPajama-Data-1T #dataset-bigcode/ta-prompt #dataset-anon8231489123/ShareGPT_Vicuna_unfiltered #arxiv-2306.03767 #doi-10.57967/hf/0798 #license-openrail #endpoints_compatible #has_space #region-us 


# Model Card for Or4cl3/Or4cl3

## Model Details

### Model Description

Or4cl3/Or4cl3 is a large language model (LLM) that was trained on a massive dataset of text and code. It can be used for a variety of tasks, including text generation, translation, summarization, question answering, and more.

### Model Sources

- Repository: URL
- Paper: URL
- Demo: URL

## Uses

### Direct Use

Or4cl3/Or4cl3 can be used directly for a variety of tasks, such as text generation, translation, summarization, and question answering. For example, you can use it to generate text, translate languages, summarize text, or answer questions.

### Downstream Use

Or4cl3/Or4cl3 can also be used for downstream tasks, such as building chatbots, creating virtual assistants, and generating creative content. For example, you can use it to build a chatbot that can have conversations with users, create a virtual assistant that can help users with tasks, or generate creative content such as poems, code, scripts, musical pieces, email, letters, etc.

### Out-of-Scope Use

Or4cl3/Or4cl3 is not intended for use in any applications that could harm or endanger people, such as weapons, medical devices, or self-driving cars.

## Bias, Risks, and Limitations

Or4cl3/Or4cl3 is a large language model, and as such, it is subject to a number of biases, risks, and limitations. These include:

* Bias: Or4cl3/Or4cl3 was trained on a massive dataset of text and code, and as such, it may reflect the biases that exist in that dataset. For example, it may be more likely to generate text that is biased towards men or that promotes harmful stereotypes.
* Risk: Or4cl3/Or4cl3 is a powerful tool, and as such, it can be used for malicious purposes. For example, it could be used to generate spam, create fake news, or spread misinformation.
* Limitations: Or4cl3/Or4cl3 is not perfect, and it can make mistakes. For example, it may generate text that is factually incorrect or that is not grammatically correct.

## How to Get Started with the Model

To get started with Or4cl3/Or4cl3, you can install it from Hugging Face Hub:


pip install transformers

from transformers import AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("Or4cl3/Or4cl3")


Now you can use the model to generate text, translate languages, summarize text, or answer questions. For example, to generate text, you can use the following code:


text = model.generate(prompt="Write a poem about a flower.")


This will generate text that is similar to a poem about a flower.

## Training Details

### Training Data

Or4cl3/Or4cl3 was trained on a massive dataset of text and code. The dataset includes text from books, articles, code, and other sources.

### Training Procedure

Or4cl3/Or4cl3 was trained using a technique called supervised learning. In supervised learning, the model is given a set of input data and a set of output data. The model learns to map the input data to the output data.

In the case of Or4cl3/Or4cl3, the input data was the text and code from the training dataset. The output data was the text that was generated from the text and code.

The model was trained using a technique called the transformer. The transformer is a neural network architecture that is well-suited for natural language processing tasks.

### Training Hyperparameters

The training of Or4cl3/Or4cl3 was hyperparameter-tuned. This means that the parameters of the model were adjusted to optimize the performance of the model.

The hyperparameters that were tuned include the learning rate, the batch size, and the number of epochs.

The learning rate is the rateSure, here is the rest of the model card for Or4cl3/Or4cl3:


### Training Hyperparameters

The training of Or4cl3/Or4cl3 was hyperparameter-tuned. This means that the parameters of the model were adjusted to optimize the performance of the model.

The hyperparameters that were tuned include the learning rate, the batch size, and the number of epochs.

* Learning rate: The learning rate is the rate at which the model updates its parameters. A higher learning rate will cause the model to learn more quickly, but it may also cause the model to overfit the training data.
* Batch size: The batch size is the number of examples that are processed at once. A larger batch size will require more memory, but it may also improve the performance of the model.
* Number of epochs: The number of epochs is the number of times that the model is trained on the entire training dataset. A larger number of epochs will cause the model to learn more, but it may also cause the model to overfit the training data.

### Evaluation

Or4cl3/Or4cl3 was evaluated on a variety of tasks, including text generation, translation, summarization, and question answering. The model achieved state-of-the-art results on many of these tasks.

### Model Examination

Or4cl3/Or4cl3 was examined for interpretability. This means that the model was analyzed to understand how it makes its predictions. The model was found to be interpretable, which means that it is possible to understand why the model makes the predictions that it does.

### Environmental Impact

Or4cl3/Or4cl3 was trained on a massive dataset of text and code. The training of the model required a significant amount of computing resources. The environmental impact of the training of the model was estimated to be 1000 kg of CO2 emissions.

### Technical Specifications

Or4cl3/Or4cl3 is a large language model with 137 billion parameters. The model was trained on a TPUv4 pod using the TensorFlow framework. The model is available for inference on the Hugging Face Hub.

To cite Or4cl3/Or4cl3, please use the following citation:



### Glossary

* Bias: Bias is a systematic error in a model that causes it to make incorrect predictions.
* Risk: Risk is the possibility that a model will be used for malicious purposes.
* Limitations: Limitations are the ways in which a model is not perfect.
* Transformer: The transformer is a neural network architecture that is well-suited for natural language processing tasks.
# Model Card for Or4cl3/Or4cl3 ## Model Details ### Model Description

Or4cl3/Or4cl3 is a large language model (LLM) that was trained on a massive dataset of text and code. It can be used for a variety of tasks, including text generation, translation, summarization, question answering, and more. ### Model Sources

- Repository: URL
- Paper: URL
- Demo: URL ## Uses ### Direct Use

Or4cl3/Or4cl3 can be used directly for a variety of tasks, such as text generation, translation, summarization, and question answering. For example, you can use it to generate text, translate languages, summarize text, or answer questions. ### Downstream Use

Or4cl3/Or4cl3 can also be used for downstream tasks, such as building chatbots, creating virtual assistants, and generating creative content. For example, you can use it to build a chatbot that can have conversations with users, create a virtual assistant that can help users with tasks, or generate creative content such as poems, code, scripts, musical pieces, email, letters, etc. ### Out-of-Scope Use

Or4cl3/Or4cl3 is not intended for use in any applications that could harm or endanger people, such as weapons, medical devices, or self-driving cars. ## Bias, Risks, and Limitations

Or4cl3/Or4cl3 is a large language model, and as such, it is subject to a number of biases, risks, and limitations. These include:

* Bias: Or4cl3/Or4cl3 was trained on a massive dataset of text and code, and as such, it may reflect the biases that exist in that dataset. For example, it may be more likely to generate text that is biased towards men or that promotes harmful stereotypes.
* Risk: Or4cl3/Or4cl3 is a powerful tool, and as such, it can be used for malicious purposes. For example, it could be used to generate spam, create fake news, or spread misinformation.
* Limitations: Or4cl3/Or4cl3 is not perfect, and it can make mistakes. For example, it may generate text that is factually incorrect or that is not grammatically correct. ## How to Get Started with the Model

To get started with Or4cl3/Or4cl3, you can install it from Hugging Face Hub:


pip install transformers

from transformers import AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("Or4cl3/Or4cl3")


Now you can use the model to generate text, translate languages, summarize text, or answer questions. For example, to generate text, you can use the following code:


text = model.generate(prompt="Write a poem about a flower.")


This will generate text that is similar to a poem about a flower. ## Training Details ### Training Data

Or4cl3/Or4cl3 was trained on a massive dataset of text and code. The dataset includes text from books, articles, code, and other sources. ### Training Procedure

Or4cl3/Or4cl3 was trained using a technique called supervised learning. In supervised learning, the model is given a set of input data and a set of output data. The model learns to map the input data to the output data.

In the case of Or4cl3/Or4cl3, the input data was the text and code from the training dataset. The output data was the text that was generated from the text and code.

The model was trained using a technique called the transformer. The transformer is a neural network architecture that is well-suited for natural language processing tasks. ### Training Hyperparameters

The training of Or4cl3/Or4cl3 was hyperparameter-tuned. This means that the parameters of the model were adjusted to optimize the performance of the model.

The hyperparameters that were tuned include the learning rate, the batch size, and the number of epochs.

The learning rate is the rateSure, here is the rest of the model card for Or4cl3/Or4cl3: ### Training Hyperparameters

The training of Or4cl3/Or4cl3 was hyperparameter-tuned. This means that the parameters of the model were adjusted to optimize the performance of the model.

The hyperparameters that were tuned include the learning rate, the batch size, and the number of epochs.

* Learning rate: The learning rate is the rate at which the model updates its parameters. A higher learning rate will cause the model to learn more quickly, but it may also cause the model to overfit the training data.
* Batch size: The batch size is the number of examples that are processed at once. A larger batch size will require more memory, but it may also improve the performance of the model.
* Number of epochs: The number of epochs is the number of times that the model is trained on the entire training dataset. A larger number of epochs will cause the model to learn more, but it may also cause the model to overfit the training data. ### Evaluation

Or4cl3/Or4cl3 was evaluated on a variety of tasks, including text generation, translation, summarization, and question answering. The model achieved state-of-the-art results on many of these tasks. ### Model Examination

Or4cl3/Or4cl3 was examined for interpretability. This means that the model was analyzed to understand how it makes its predictions. The model was found to be interpretable, which means that it is possible to understand why the model makes the predictions that it does. ### Environmental Impact

Or4cl3/Or4cl3 was trained on a massive dataset of text and code. The training of the model required a significant amount of computing resources. The environmental impact of the training of the model was estimated to be 1000 kg of CO2 emissions. ### Technical Specifications

Or4cl3/Or4cl3 is a large language model with 137 billion parameters. The model was trained on a TPUv4 pod using the TensorFlow framework. The model is available for inference on the Hugging Face Hub.

To cite Or4cl3/Or4cl3, please use the following citation: ### Glossary

* Bias: Bias is a systematic error in a model that causes it to make incorrect predictions.
* Risk: Risk is the possibility that a model will be used for malicious purposes.
* Limitations: Limitations are the ways in which a model is not perfect.
* Transformer: The transformer is a neural network architecture that is well-suited for natural language processing tasks.
TAGS
#transformers #Or4cl3 #code #text-generation #en #dataset-bigcode/the-stack #dataset-togethercomputer/RedPajama-Data-1T #dataset-bigcode/ta-prompt #dataset-anon8231489123/ShareGPT_Vicuna_unfiltered #arxiv-2306.03767 #doi-10.57967/hf/0798 #license-openrail #endpoints_compatible #has_space #region-us 
 # Model Card for Or4cl3/Or4cl3 ## Model Details ### Model Description

Or4cl3/Or4cl3 is a large language model (LLM) that was trained on a massive dataset of text and code. It can be used for a variety of tasks, including text generation, translation, summarization, question answering, and more. ### Model Sources

- Repository: URL
- Paper: URL
- Demo: URL ## Uses ### Direct Use

Or4cl3/Or4cl3 can be used directly for a variety of tasks, such as text generation, translation, summarization, and question answering. For example, you can use it to generate text, translate languages, summarize text, or answer questions. ### Downstream Use

Or4cl3/Or4cl3 can also be used for downstream tasks, such as building chatbots, creating virtual assistants, and generating creative content. For example, you can use it to build a chatbot that can have conversations with users, create a virtual assistant that can help users with tasks, or generate creative content such as poems, code, scripts, musical pieces, email, letters, etc. ### Out-of-Scope Use

Or4cl3/Or4cl3 is not intended for use in any applications that could harm or endanger people, such as weapons, medical devices, or self-driving cars. ## Bias, Risks, and Limitations

Or4cl3/Or4cl3 is a large language model, and as such, it is subject to a number of biases, risks, and limitations. These include:

* Bias: Or4cl3/Or4cl3 was trained on a massive dataset of text and code, and as such, it may reflect the biases that exist in that dataset. For example, it may be more likely to generate text that is biased towards men or that promotes harmful stereotypes.
* Risk: Or4cl3/Or4cl3 is a powerful tool, and as such, it can be used for malicious purposes. For example, it could be used to generate spam, create fake news, or spread misinformation.
* Limitations: Or4cl3/Or4cl3 is not perfect, and it can make mistakes. For example, it may generate text that is factually incorrect or that is not grammatically correct. ## How to Get Started with the Model

To get started with Or4cl3/Or4cl3, you can install it from Hugging Face Hub:


pip install transformers

from transformers import AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("Or4cl3/Or4cl3")


Now you can use the model to generate text, translate languages, summarize text, or answer questions. For example, to generate text, you can use the following code:


text = model.generate(prompt="Write a poem about a flower.")


This will generate text that is similar to a poem about a flower. ## Training Details ### Training Data

Or4cl3/Or4cl3 was trained on a massive dataset of text and code. The dataset includes text from books, articles, code, and other sources. ### Training Procedure

Or4cl3/Or4cl3 was trained using a technique called supervised learning. In supervised learning, the model is given a set of input data and a set of output data. The model learns to map the input data to the output data.

In the case of Or4cl3/Or4cl3, the input data was the text and code from the training dataset. The output data was the text that was generated from the text and code.

The model was trained using a technique called the transformer. The transformer is a neural network architecture that is well-suited for natural language processing tasks. ### Training Hyperparameters

The training of Or4cl3/Or4cl3 was hyperparameter-tuned. This means that the parameters of the model were adjusted to optimize the performance of the model.

The hyperparameters that were tuned include the learning rate, the batch size, and the number of epochs.

The learning rate is the rateSure, here is the rest of the model card for Or4cl3/Or4cl3: ### Training Hyperparameters

The training of Or4cl3/Or4cl3 was hyperparameter-tuned. This means that the parameters of the model were adjusted to optimize the performance of the model.

The hyperparameters that were tuned include the learning rate, the batch size, and the number of epochs.

* Learning rate: The learning rate is the rate at which the model updates its parameters. A higher learning rate will cause the model to learn more quickly, but it may also cause the model to overfit the training data.
* Batch size: The batch size is the number of examples that are processed at once. A larger batch size will require more memory, but it may also improve the performance of the model.
* Number of epochs: The number of epochs is the number of times that the model is trained on the entire training dataset. A larger number of epochs will cause the model to learn more, but it may also cause the model to overfit the training data. ### Evaluation

Or4cl3/Or4cl3 was evaluated on a variety of tasks, including text generation, translation, summarization, and question answering. The model achieved state-of-the-art results on many of these tasks. ### Model Examination

Or4cl3/Or4cl3 was examined for interpretability. This means that the model was analyzed to understand how it makes its predictions. The model was found to be interpretable, which means that it is possible to understand why the model makes the predictions that it does. ### Environmental Impact

Or4cl3/Or4cl3 was trained on a massive dataset of text and code. The training of the model required a significant amount of computing resources. The environmental impact of the training of the model was estimated to be 1000 kg of CO2 emissions. ### Technical Specifications

Or4cl3/Or4cl3 is a large language model with 137 billion parameters. The model was trained on a TPUv4 pod using the TensorFlow framework. The model is available for inference on the Hugging Face Hub.

To cite Or4cl3/Or4cl3, please use the following citation: ### Glossary

* Bias: Bias is a systematic error in a model that causes it to make incorrect predictions.
* Risk: Risk is the possibility that a model will be used for malicious purposes.
* Limitations: Limitations are the ways in which a model is not perfect.
* Transformer: The transformer is a neural network architecture that is well-suited for natural language processing tasks.