| --- |
| license: mit |
| datasets: |
| - CodeTranslatorLLM/Code-Translation |
| language: |
| - en |
| base_model: |
| - unsloth/Llama-3.2-3B-Instruct |
| new_version: meta-llama/Llama-3.2-3B-Instruct |
| pipeline_tag: text2text-generation |
| tags: |
| - Fortran |
| - Rust |
| - FortranToRust |
| --- |
| # Model Card for Model ID |
|
|
| <!-- Provide a quick summary of what the model is/does. --> |
|
|
| This modelcard aims to be a base template for new models. It has been generated using [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct). |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| CodeConvLLM is a language model specifically designed to translate OCaml and Fortran code into C# and Rust. It integrates seamlessly with Visual Studio Code through a plugin and ensures industry-standard translations by benchmarking outputs against best practices in software engineering. |
|
|
|
|
| - **Developed by:** [Caslow Chien](https://huggingface.co/Caslow) and [Chandrahas Aroori](https://huggingface.co/charoori) |
| - **License:** MIT |
| - **Finetuned from model:** [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) |
|
|
| ### Model Sources |
|
|
| - **Repository:** [GitHub Repo](https://github.com/CodeTranslatorLLM/LinguistLLM) |
|
|
| ## Uses |
|
|
| ### Direct Use |
| The model can be used directly for code translation tasks. Users can input OCaml or Fortran code into the Gradio dashboard or via the API and receive C# or Rust code as output. It is particularly suitable for developers needing efficient, accurate language conversion in software migration or integration projects. |
|
|
| ### Downstream Use [optional] |
| Developers can integrate this model into larger IDE ecosystems, CI/CD pipelines, or automated code-refactoring systems for multi-language development projects. |
|
|
| ### Out-of-Scope Use |
| The model should not be used for: |
| + Translating non-code text. |
| + Translating between languages it is not trained for (e.g., Python to Java). |
| + Malicious purposes, such as creating obfuscated or harmful code. |
| + |
| ## Bias, Risks, and Limitations |
| While the model achieves high accuracy in most cases, it may introduce errors in certain edge cases or highly domain-specific code. Users must verify and test generated outputs thoroughly before deployment. |
|
|
| ### Recommendations |
| + Validation: Benchmark the output using standard tools and manual review. |
| + Testing: Integrate automated testing during translation workflows to catch potential bugs or inaccuracies. |
|
|
| 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 |
|
|
| Please visit the [GitHub Repo](https://github.com/CodeTranslatorLLM/LinguistLLM) for a detailed guide. |
|
|
| **Quick Guide** |
| 1. Load the model |
| ```python |
| from unsloth import FastLanguageModel |
| from transformers import TextStreamer |
| |
| max_seq_length = 2048 |
| dtype = None |
| load_in_4bit = True |
| |
| model, tokenizer = FastLanguageModel.from_pretrained( |
| model_name = "lora_model", # OUR MODEL YOU USED FOR TRAINING, put it under the same folder |
| max_seq_length = max_seq_length, |
| dtype = dtype, |
| load_in_4bit = load_in_4bit, |
| ) |
| FastLanguageModel.for_inference(model) |
| ``` |
|
|
| 2. Get your input |
| ```python |
| USER_INPUT_CODE = # YOUR FORTRAN CODE |
| USER_INPUT_EXPLANATION = # YOUR FORTRAN CODE EXPLANATION |
| messages = [ |
| { |
| "role": "user", |
| "content": str("[Fortran Code]") + str(USER_INPUT_CODE) + str("[Fortran Code Explain]" )+ str(USER_INPUT_EXPLANATION) |
| }, |
| ] |
| inputs = tokenizer.apply_chat_template( |
| messages, |
| tokenize = True, |
| add_generation_prompt = True, # Must add for generation |
| return_tensors = "pt", |
| ).to("cuda") |
| ``` |
|
|
| 3. Run the model |
| ```python |
| text_streamer = TextStreamer(tokenizer, skip_prompt = True) |
| _ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 2000, |
| use_cache = True, temperature = 1.5, min_p = 0.1) |
| ``` |
|
|
| ## Training Details |
|
|
| Please visit the [GitHub Repo](https://github.com/CodeTranslatorLLM/LinguistLLM) for a detailed guide. |
|
|
| ### Training Data |
|
|
| Please visit the [GitHub Repo](https://github.com/CodeTranslatorLLM/LinguistLLM) for a detailed guide. |
|
|
| ### Training Procedure |
|
|
| Please visit the [GitHub Repo](https://github.com/CodeTranslatorLLM/LinguistLLM) for a detailed guide. |
