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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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Downstream Use [optional]

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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

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

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]

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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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Technical Specifications [optional]

Model Architecture and Objective

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Software

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Model size
125M params
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F32
FP16
I8
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