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gpt2-javis-stks

Text Generation
Transformers
Safetensors
gpt2
text-generation-inference
Inference Endpoints
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metadata
library_name: transformers
tags: []

Model Card for Model ID

Fine tuning (learning/educational) results of GPT2-medium on a customized dataset

Testing different ways to provide a though process withing GPT2

This one seemed like the best results for now..

Model Details

Base model : 'hf_models/gpt2-medium'

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Uses

Direct Use 

from transformers import GPT2LMHeadModel, GPT2Tokenizer

models_folder = "Deeokay/gpt2-javis-stks"

# if you know your device, you can just set "device = 'mps'" 
device = torch.device("cuda" if torch.cuda.is_available90 else "cpu")

model = GPT2LMHeadModel.from_pretrained(models_folder)
tokenizer = GPT2Tokenizer.from_pretrained(models_folder)

tokenizer.pad_token = tokenizer.eos_token

prompt = "what is the meaning of life?"

inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True).to(device)

set_seed(42)

sample_output = model.generate(
    **inputs,
    max_new_tokens=250,
    do_sample=True,
    top_k=30,
    temperature=0.7,
)

print("Output:\n" + 80 * '-')
print(tokenizer.decode(sample_output[0], skip_special_tokens=True))

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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

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Training Hyperparameters

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Evaluation

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Results

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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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