metadata

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

Model Card for Model ID

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

    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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Out-of-Scope Use

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Bias, Risks, and Limitations

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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.

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Training Details

Training Data

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Training Procedure

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Summary

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Environmental Impact

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

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