multilingual-doc-assistant

Multilingual Document Assistant

Agent-style model for explaining documents, answering questions, and responding conversationally in:

• Spanish
• Chinese
• Vietnamese
• Portuguese

Base model: bigscience/bloom-560m on Hugging Face.

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Run on Hugging Face

To run this as a Hugging Face Space (browser chat UI):

1. Create a Space at huggingface.co/new-space:

   • Choose Gradio.
   • Clone or upload this repo (at least app.py and requirements.txt).

2. Use your fine-tuned model (after training and pushing):

   • Train: python train.py
   • Push to Hub: export HF_REPO_ID=your-username/multilingual-doc-assistant then python push_to_hub.py
   • In the Space, go to Settings → Variables and add:
     • HF_MODEL_ID = your-username/multilingual-doc-assistant
   • The app will load your model from the Hub. Without this, it uses the base BLOOM model.

3. The Space runs app.py and serves the Gradio chat interface.

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Setup (local)

cd multilingual-doc-assistant
pip install -r requirements.txt

Train

python train.py

Saves the fine-tuned model and tokenizer to ./multilingual-doc-model. You can run from any directory; paths are relative to the script.

Test / Chat

After training:

python test_model.py

Uses a Spanish prompt by default. You can edit the prompt in test_model.py to try other languages or questions.

Training data

Add more examples in train.jsonl (one JSON object per line with a "text" key). Use the same User: / Assistant: format so the model learns the conversational style.

Run the Space UI locally

pip install -r requirements.txt
python app.py

Then open the URL Gradio prints (e.g. http://127.0.0.1:7860). To use your trained model locally, set HF_MODEL_ID to a Hub repo or a local path; for a local folder use the path to multilingual-doc-model (transformers supports local paths).