
# Model Card for alias-gpt2-small-x21 
 
# Model Details
 
## Model Description
 
More information needed
 
- **Developed by:** Stanford CRFM
- **Shared by [Optional]:** Stanford CRFM

- **Model type:** Text Generation
- **Language(s) (NLP):** More information needed
- **License:** Apache 2.0
- **Parent Model:** [GPT-2](https://huggingface.co/gpt2?text=My+name+is+Thomas+and+my+main)
- **Resources for more information:** 
    - [GitHub Repo](https://github.com/stanford-crfm/mistral)



# Uses
 

## Direct Use
This model can be used for the task of Text Generation.
 
## Downstream Use [Optional]
 
More information needed.
 
## Out-of-Scope Use
 
The model should not be used to intentionally create hostile or alienating environments for people. 
 
# Bias, Risks, and 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
 
 
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

# Training Details
 
## Training Data
 
More information 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](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

 
**BibTeX:**
 
More information needed




**APA:**

More information needed
  
# Glossary [optional]
 
More information needed

# More Information [optional]
More information needed

# Model Card Authors [optional]
 
Stanford CRFM  in collaboration with Ezi Ozoani and the Hugging Face team

# 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>

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stanford-crfm/alias-gpt2-small-x21")

model = AutoModelForCausalLM.from_pretrained("stanford-crfm/alias-gpt2-small-x21")
 ```
</details>

{"license": "apache-2.0", "tags": ["gpt2", "text-generation"]}
transformers pytorch gpt2 text-generation arxiv:1910.09700 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# Model Card for alias-gpt2-small-x21 
 
# Model Details
 
## Model Description
 
More information needed
 
- Developed by: Stanford CRFM
- Shared by [Optional]: Stanford CRFM

- Model type: Text Generation
- Language(s) (NLP): More information needed
- License: Apache 2.0
- Parent Model: GPT-2
- Resources for more information: 
    - GitHub Repo



# Uses
 

## Direct Use
This model can be used for the task of Text Generation.
 
## Downstream Use [Optional]
 
More information needed.
 
## Out-of-Scope Use
 
The model should not be used to intentionally create hostile or alienating environments for people. 
 
# 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
 
 
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

# Training Details
 
## Training Data
 
More information 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]
 
Stanford CRFM  in collaboration with Ezi Ozoani and the Hugging Face team

# 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>


</details>

# Model Card for alias-gpt2-small-x21 # Model Details ## Model Description
 
More information needed
 
- Developed by: Stanford CRFM
- Shared by [Optional]: Stanford CRFM

- Model type: Text Generation
- Language(s) (NLP): More information needed
- License: Apache 2.0
- Parent Model: GPT-2
- Resources for more information: 
    - GitHub Repo # Uses ## Direct Use
This model can be used for the task of Text Generation. ## Downstream Use [Optional]
 
More information needed. ## Out-of-Scope Use
 
The model should not be used to intentionally create hostile or alienating environments for people. # 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
 
 
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data
 
More information 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]
 
Stanford CRFM  in collaboration with Ezi Ozoani and the Hugging Face team # 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>


</details>
TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Model Card for alias-gpt2-small-x21 # Model Details ## Model Description
 
More information needed
 
- Developed by: Stanford CRFM
- Shared by [Optional]: Stanford CRFM

- Model type: Text Generation
- Language(s) (NLP): More information needed
- License: Apache 2.0
- Parent Model: GPT-2
- Resources for more information: 
    - GitHub Repo # Uses ## Direct Use
This model can be used for the task of Text Generation. ## Downstream Use [Optional]
 
More information needed. ## Out-of-Scope Use
 
The model should not be used to intentionally create hostile or alienating environments for people. # 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
 
 
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data
 
More information 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]
 
Stanford CRFM  in collaboration with Ezi Ozoani and the Hugging Face team # 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>


</details>
passage: TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Model Card for alias-gpt2-small-x21# Model Details## Model Description
 
More information needed
 
- Developed by: Stanford CRFM
- Shared by [Optional]: Stanford CRFM

- Model type: Text Generation
- Language(s) (NLP): More information needed
- License: Apache 2.0
- Parent Model: GPT-2
- Resources for more information: 
    - GitHub Repo# Uses## Direct Use
This model can be used for the task of Text Generation.## Downstream Use [Optional]
 
More information needed.## Out-of-Scope Use
 
The model should not be used to intentionally create hostile or alienating environments for people.# 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
 
 
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.# Training Details## Training Data
 
More information 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