---
library_name: transformers
license: mit
language:
- en
pipeline_tag: text-generation
---

# Model Card for Model ID

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



## Model Details

Don't use the Inference API ( the input box to the right of this page), as that is not optimized for this generation. ---->

Please use the code provided below.


### Uses

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
```python
    import transformers
    from transformers import AutoModelForCausalLM, AutoTokenizer
    model_name = "rony/gpt2-quantized-jokes"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    prompt = tokenizer.apply_chat_template('JOKE: ', add_generation_prompt=True, tokenize=False)

    # Create pipeline
    pipeline = transformers.pipeline(
        "text-generation",
        model=model_name,
        tokenizer=tokenizer
    )

    # Generate text
    sequences = pipeline(
        prompt,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
        num_return_sequences=1,
        max_length=200,
    )
    print(sequences[0]['generated_text'])
```

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### Downstream Use [optional]

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### Out-of-Scope Use

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

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## Bias, Risks, and Limitations

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### Recommendations

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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.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### 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]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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

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## 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

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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