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https://github.com/shrut2702/upasak

UI-based Fine-Tuning for Large Language Models (LLMs)
https://github.com/shrut2702/upasak

gemma gemma3 largelanguagemodels llm llm-training nlp no-code-framework open-source pii-detection transformers

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UI-based Fine-Tuning for Large Language Models (LLMs)

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README 

          



# Upasak - UI-based Fine-Tuning for Large Language Models (LLMs)



**Upasak** is a flexible, mindful to privacy, no-code/low-code framework for fine-tuning large language models, built around [Hugging Face Transformers](https://huggingface.co/docs/transformers/en/index).

It features an easy-to-use Streamlit-based interface, multi-format dataset support, built-in PII and sensitive information sanitization, and a customizable training process.

 Whether you're experimenting, researching, or performing internal fine-tuning tasks, Upasak makes it easily accessible and compliant.





  

    PyPI Version

  

  

    License

  






  




## **Key Features**



### **LLM Fine-Tuning**

* Developed on top of Hugging Face's Transformers library.

* Supports Text-only models of Gemma-3 LLM family for instruction-tuning or domain adaptation.

* Full-parameter fine-tuning or LoRA (Parameter-Efficient Fine-Tuning).

* Future support planned for image-text-to-text Gemma-3 models, LLaMA, Qwen, Phi, Mixtral.



### **Flexible Dataset Handling**

Upload or import datasets in multiple file formats:



* `.json`

* `.jsonl`

* `.csv`

* `.zip` (containing `.txt`)



Or select datasets directly from the **Hugging Face Hub**.



### **Auto-Detection of Dataset Schema**



Upasak intelligently identifies and structures your dataset into training-ready format.

Supported schemas:



| Schema              | Format                                            | Notes                                       |

| ------------------- | -------------------------------------------------- | ------------------------------------------- |

| **DAPT**            | `[{"text":"..."}]` or `text` column                                         | Document Adaptation / continued pretraining |

| **ALPACA**          | `[{"instruction":"...", "output":"..."}]` (+ optional `"input"`) or `instruction`, `output`, `input` (optional) columns | Converted to user → assistant turns         |

| **CHATML**          | `[{"messages":[{"role":"...", "content":"..."}]}]` or `messages` column                                     | Supports role/content pairs                 |

| **SHARE_GPT**       | `[{"conversations":[{"from":"...", "value":"..."}]}]` or `conversations` column                                | Converts human ↔ model to user ↔ assistant  |

| **PROMPT_RESPONSE** | `[{"prompt":"...", "response":"..."}]` or `prompt`, `response` columns                           | Simple instruction → answer                 |

| **QA**              | `[{"question":"", "answer":""}]` or `question`, `answer` columns                           | Q&A format                          |

| **QLA**             | `[{"question":"...", "long_answer":"..."}]` or `question`, `long_answer` columns                      | Long-form generation                        |



### **Built-In PII & Sensitive Information Sanitization**



Upasak ensures privacy compliance by:



* Automatically detecting and redacting/masking PII

* Using placeholder tokens to preserve dataset utility

* Offering AI-assisted detection with manual review loops, which uses [GLiNER](https://huggingface.co/urchade/gliner_multi_pii-v1) (Named Entity Recognition) model.

* Logging sanitization results for auditability



Upasak automatically detects and redacts:

* Personal names

* Emails / phone numbers

* IP addresses, IMEI

* Credit card / bank details

* National IDs (Aadhaar, PAN, Voter ID)

* API keys

* GitHub/GitLab tokens

* Database credentials

* Residential & workplace addresses



Two sanitization modes:



1. **Rule-Based** (default)

2. **Hybrid (Rule-Based + NER-based)**



   * Optional human review

   * Configure HITL ratio & max samples for human review

   * Accept/reject uncertain detections directly in the UI

   * Preview sanitized sample before training

---



## **Streamlit UI – No-Code Training Workflow**





  




The visual interface provides fully interactive control:



### **1. Model Selection**



Choose supported base models (currently Gemma-3 text-only).

Future updates will include LLaMA, Mixtral, Phi, Qwen and multimodal variants.



### **2. HF Token Handling**



* Read token for pulling models

* Write token for pushing fine-tuned models back to HF Hub



### **3. Dataset Input**



* Upload dataset files

* Or load from Hugging Face dataset list



### **4. PII Sanitization Panel**



* Enable/disable sanitization

* Select detection method (rule-based / hybrid)

* Enable Human Review & configure ratios

* View uncertain detections and choose actions

* Preview sanitized sample before training



### **5. Hyperparameter Controls**



#### **Basic Hyperparameters**



* Learning rate

* Batch size

* Epochs

* Max sequence length

* Logging steps

* LR scheduler



#### **Advanced Hyperparameters**



* Gradient accumulation

* Gradient clipping

* LR warmup ratio

* Weight decay

* Checkpoint save strategy

* Evaluation strategy + steps

* Validation split

* Model tracker platform (Comet / WandB / none)

* Tracker API keys



### **6. LoRA Configuration**



* LoRA rank

* LoRA alpha

* LoRA dropout

* Target modules

* Optional merging of LoRA adapters



### **7. Training Control**



* Start / Stop training

* Live training metrics inside the app:



  * Training loss

  * Validation loss

  * Token-level curves

* Optional external tracking (Comet / WandB)



### **8. Inference Script Generation**

After training completes, Upasak automatically generates a customized inference.py script tailored to your training configuration.



* **LoRA support** – Handles both scenarios:

    * **LoRA + merged adapters** – Loads the fully merged model.

    * **LoRA + unmerged adapters** – Loads base model + applies LoRA adapters at runtime.

    * **Full fine-tune** – Standard model loading

* **Ready to use** - Access it in your output directory



**Usage**



```bash

cd path_to_output_dir

python inference.py

```



### **9. Export & Push**



* Output directory for checkpoints, final model, and merged model

* Push to HF Hub (when write-enabled token is provided)



---



# **Installation**



### **Install from PyPI (recommended)**



```bash

pip install upasak

```



### **Or install from source**



```bash

# Clone this repo

git clone https://github.com/shrut2702/upasak

cd upasak

```

```bash

# optional



## For Windows

python -m venv vir_env

./vir_env/scripts/activate



## For macOS

python -m venv vir_env

source vir_env/bin/activate

```



```bash

# Install required dependencies

pip install -r requirements.txt

```



---



## **Usage**



Upasak is used as a Python-triggered Streamlit app.



### **After installing the package:**



#### **1. Create a Python launcher file**



For example: `run_upasak.py`



```python

from upasak import main



if __name__ == "__main__":

    main()

```



#### **2. Launch the Streamlit application**



```bash

streamlit run run_upasak.py

```



or 



```bash

streamlit run run_upasak.py --server.maxUploadSize=1024 # for configuring upload file size limit in MB

```



This opens the Upasak UI in your browser.



### **After installing from source**



#### **1. Launch `app.py`**

```bash

streamlit run app.py

```



or 



```bash

streamlit run app.py --server.maxUploadSize=1024 # for configuring upload file size limit in MB

```



### **Reusability of Upasak Modules**



Although Upasak provides a full end-to-end UI, **every internal component is designed to be reusable in isolation**.

You can import and use modules such as:



* `TokenizerWrapper` → standalone tokenization 

* `TrainingEngine` + `TrainerConfig` → run full or LoRA fine-tuning programmatically

* `PIISanitizer` → rule-based or hybrid PII detection/sanitization



You can refer to [examples](https://github.com/shrut2702/upasak/tree/f4252b2e2072aad9e878005108abc564d8b670a0/examples) to more details.



This allows you to integrate Upasak **directly into custom pipelines**, backend services, notebooks, or data-processing workflows — **without launching the Streamlit UI**.



---



# **Use Cases**



* Educational fine-tuning demonstrations 

* Rapid prototyping in quick-shipping environments

* Dataset preparation and anonymization workflows

* Internal LLM finetuning on sensitive or regulated data

* Developers with no domain expertise who wants LLM in their application



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# **Contributing**



Contributions are welcome!

Please open an issue or submit a pull request for bug fixes, features, documentation, or dataset schema support.



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# **Support**



For issues, questions, or feature requests:

Create a GitHub issue in this repository.



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