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--- |
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base_model: Salesforce/codegen-350M-mono |
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library_name: peft |
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license: mit |
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datasets: |
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- google/code_x_glue_ct_code_to_text |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# CodeGen-ft-python |
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<!-- Provide a quick summary of what the model is/does. --> |
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Generate python code from natural language prompts. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This model is a fine-tuned variant of Salesforce/codegen-350M-mono, |
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specialized for natural language to code generation in Python. |
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It takes natural language instructions (e.g., “check MySQL database connection”) |
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and generates the corresponding Python code snippet. |
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The model was trained on a curated text-to-code dataset containing diverse |
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programming instructions and function-level examples to improve semantic and syntactic accuracy. |
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- **Developed by:** Akshay Bharadwaj |
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- **Model type:** Transformer-based Causal Language Model |
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- **Language(s) (NLP):** English (Prompts) and Python (Code Outputs) |
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- **License:** MIT License |
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- **Finetuned from model [optional]:** Salesforce/codegen-350M-mono |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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The model can be used for: |
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* Translating natural language prompts into functional Python code. |
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* Assisting in code autocompletion or boilerplate generation. |
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* Supporting educational and prototyping environments. |
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### Downstream Use |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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Can be integrated into: |
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* Developer tools (IDE plugins or assistants). |
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* Chatbots for code assistance or educational coding tutors. |
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* LLM pipelines for multi-step reasoning or coding workflows. |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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* Generating production-level code without human review. |
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* Security-critical or real-time applications (e.g., code execution automation). |
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* Generation of malicious or unsafe code. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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* The model may produce incomplete or syntactically incorrect code for ambiguous prompts. |
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* It can misinterpret vague natural language queries (semantic drift). |
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* Potential bias toward common Python idioms and limited handling of rare libraries or APIs. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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``` |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_id = "akshayb/nl-code-gen-python" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id) |
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prompt = "write a python function to check mysql database connection" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=256) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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The dataset contains paired natural language descriptions |
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and Python function implementations, collected and cleaned |
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from public code repositories and text-to-code benchmarks (e.g., CodeXGLUE). |
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Preprocessing involved deduplication, tokenization, and removal of incomplete code samples. |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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For Comparison between Base Model and Fine-tuned model, we use the following metrics: |
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| Metric | Focus | Strength | |
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| ---------------- | ------------------------------ | ----------------------------------------- | |
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| **BLEU** | Token-level similarity | Measures fluency and lexical accuracy | |
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| **CodeBLEU** | Lexical + syntactic + semantic | Captures holistic code quality | |
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| **Exact Match** | String equality | Strict correctness measure | |
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| **Syntax Match** | AST structure | Validates syntactic and logical integrity | |
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## Citation [optional] |
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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 |
