Re-add tokenizer.json with LFS

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.

lora_model/README.md

@@ -1,202 +0,0 @@
----
-base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
-library_name: peft
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-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
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.13.2