Instructions to use akshaybharadwaj96/nl-code-gen-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use akshaybharadwaj96/nl-code-gen-python with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-mono") model = PeftModel.from_pretrained(base_model, "akshaybharadwaj96/nl-code-gen-python") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -133,13 +133,13 @@ For Comparison between Base Model and Fine-tuned model, we use the following met
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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@misc{akshay2025nlcodegen,
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title={Natural Language to Code Generation (Fine-tuned CodeGen-350M)},
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author={Akshay Bharadwaj},
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year={2025},
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howpublished={\url{https://huggingface.co/akshayb/nl-code-gen-python}}
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}
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- PEFT 0.7.2.dev0
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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```
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@misc{akshay2025nlcodegen,
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title={Natural Language to Code Generation (Fine-tuned CodeGen-350M)},
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author={Akshay Bharadwaj},
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year={2025},
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howpublished={\url{https://huggingface.co/akshayb/nl-code-gen-python}}
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}
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```
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- PEFT 0.7.2.dev0
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