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Model: PranavGuhan/python-ds-accelerate
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- huggingface-course/codeparrot-ds-train
+- huggingface-course/codeparrot-ds-valid
+language:
+- en
+metrics:
+- code_eval
+pipeline_tag: text-generation
+tags:
+- code
+- gpt2
+- pytorch
+- causal-lm
+---
+
+# python-ds-accelerate (GPT-2 124M)
+
+This model is a GPT-2 (124M parameter) causal language model trained from scratch specifically for **Python code completion** in Data Science contexts.
+
+## Model Details
+
+### Model Description
+
+This model is an implementation of the GPT-2 architecture optimized for generating functional Python code snippets. It was trained using a custom training pipeline that incorporates a **keytoken weighted loss** function to prioritize important programming keywords (like `plt`, `pd`, `fit`, `predict`), making it more effective at suggesting Data Science-related code.
+
+- **Developed by:** [Pranav Guhan R](https://github.com/PranavGuhanR)
+- **Model type:** Transformer-based Causal Language Model
+- **Language(s):** Python (English comments)
+- **License:** Apache 2.0
+- **Finetuned from model:** Trained from scratch
+
+### Model Sources
+
+- **Repository:** [GPT-2-124M-pretraining-for-code-completion](https://github.com/PranavGuhanR/GPT-2-124M-pretraining-for-code-completion)
+
+## Uses
+
+### Direct Use
+The model is intended to be used for code completion tasks, specifically for completing Python scripts involving libraries like `pandas`, `matplotlib`, and `scikit-learn`.
+
+### Out-of-Scope Use
+The model is not suitable for general-purpose natural language conversation or generating code in languages other than Python.
+
+## How to Get Started with the Model
+
+You can use the model directly with a Hugging Face pipeline:
+
+```python
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="PranavGuhan/python-ds-accelerate")
+
+txt = """# create dataframe from x and y
+df = pd.DataFrame({'x':x, 'y':y})
+"""
+print(pipe(txt, num_return_sequences=1)[0]["generated_text"])