gpt2-quantized-jokes

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Model Details

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Uses

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    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

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Bias, Risks, and Limitations

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Recommendations

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How 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

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Summary

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Environmental Impact

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

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Technical Specifications [optional]

Model Architecture and Objective

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