Update README.md

README.md

@@ -5,5 +5,114 @@ datasets:
 language:
 - en
 base_model:
-- meta-llama/Llama-3.2-3B-Instruct
----
+- 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.