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--- |
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base_model: |
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- openai-community/gpt2 |
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language: |
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- en |
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- ta |
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license: mit |
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tags: |
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- gpt2 |
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- text-generation |
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- QnQ |
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datasets: |
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- varshil27/1mg-train-data-LLama2-formatted |
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- karthikqnq/1mgdataset |
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- anjandash/java-8m-methods-v2 |
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metrics: |
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- accuracy |
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--- |
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# QnQGPT Model |
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This is a custom GPT model based on GPT-2 architecture. |
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## Model Details |
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- Model Type: GPT-2 |
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- Base Model: gpt2 |
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- Training Data: [Describe your training data] |
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- Use Cases: [Describe intended use cases] |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("karthikqnq/qnqgpt") |
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tokenizer = AutoTokenizer.from_pretrained("karthikqnq/qnqgpt") |
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# Generate text |
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text = "Hello, how are" |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=50) |
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result = tokenizer.decode(outputs[0]) |
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print(result) |
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``` |
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## Training Details |
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[Add your training details here] |
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## Limitations |
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[Add model limitations here] |
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## License |
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This model is released under the MIT License. |
