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license: apache-2.0

TensorTalk / UM_Handbook

TensorTalk is a handbook-grounded academic chat assistant built for the Faculty of Computer Science and Information Technology, Universiti Malaya (UM).

This project focuses on turning UM handbook content into a usable question-answering system through:

  • handbook preprocessing
  • source chunk construction
  • supervised QA dataset building
  • Qwen3-8B LoRA fine-tuning
  • merged-model deployment
  • a browser-style HTML chat demo

Project Goal

The main goal of this project is to build a handbook-based assistant that can answer student questions using information learned from the UM handbook domain.

The current version is designed around:

  • undergraduate and postgraduate handbook content
  • handbook-faithful answers
  • concise student-facing responses
  • a local/demo deployment workflow on DICC and notebook environments

This project is also intended to support a broader experimental pipeline:

  • Baseline 1: closed-book supervised fine-tuning
  • Baseline 2: retrieval-augmented version for later comparison

What This Project Contains

1. Dataset Preparation

The project includes scripts and resources for preparing handbook data before fine-tuning:

  • handbook markdown preprocessing
  • source chunk dataset building
  • SFT QA dataset construction
  • configuration management for the preprocessing and dataset pipeline

2. Fine-Tuning Workflow

The model training workflow uses a Qwen3-8B base model with LoRA-based fine-tuning on the UM handbook QA dataset.

The fine-tuning workflow includes:

  • notebook-based training on DICC
  • device-aware loading logic
  • train / validation / test style evaluation workflow
  • merged-model export for direct inference
  • LoRA adapter export for optional PEFT-based reuse
  • metrics and prediction file generation

3. Deployment Demo

The project includes a notebook-based HTML chat UI called TensorTalk.

The demo provides:

  • a browser-style chat layout
  • a handbook-focused system prompt
  • merged-model loading for direct inference
  • a student-facing question-answer workflow
  • a simple deployment path for demonstration purposes

Current Project Structure 

UM_Handbook/
├── Dataset/
│   └── SFT_Dataset/
│       ├── SFT_QA_Training_Ready.jsonl
│       ├── SFT_QA_Training_Ready_pretty.json
│       ├── SFT_QA_Metadata.jsonl
│       └── SFT_QA_Metadata_pretty.json
├── assets/
├── outputs/
│   └── qwen3_um_handbook_optimized_1/
│       ├── lora_adapter/
│       ├── merged_model/
│       ├── trainer_runs/
│       ├── test_eval_runs/
│       ├── dataset_split_summary.json
│       ├── final_metrics.json
│       ├── test_predictions.jsonl
│       └── validation_predictions.jsonl
├── FineTune_QWEN3_UM_Handbook_optimized_1.ipynb
├── UM_Handbook_Markdown_Preprocess.py
├── UM_SFT_QA_Dataset_Builder_from_Index.py
├── UM_Source_Chunk_Dataset_Builder.py
└── um_handbook_config.py

Key Files

Training and Data

  • Dataset/SFT_Dataset/SFT_QA_Training_Ready.jsonl
    Main SFT training dataset used for handbook QA fine-tuning.

  • UM_Handbook_Markdown_Preprocess.py
    Preprocesses handbook markdown / extracted source text.

  • UM_Source_Chunk_Dataset_Builder.py
    Builds source chunks for downstream dataset and retrieval-related use.

  • UM_SFT_QA_Dataset_Builder_from_Index.py
    Builds the supervised QA dataset from curated handbook content.

  • um_handbook_config.py
    Central configuration file for paths and data-processing settings.

Training Output

  • outputs/qwen3_um_handbook_optimized_1/merged_model/
    Main inference-ready model directory.
    This is the directory used by the demo chat UI.

  • outputs/qwen3_um_handbook_optimized_1/lora_adapter/
    LoRA adapter weights.
    This is useful for PEFT-style loading with a base model, but it is not the primary path used by the current demo UI.

  • outputs/qwen3_um_handbook_optimized_1/final_metrics.json
    Final evaluation summary.

  • outputs/qwen3_um_handbook_optimized_1/validation_predictions.jsonl
    Validation-set generated answers for inspection.

  • outputs/qwen3_um_handbook_optimized_1/test_predictions.jsonl
    Test-set generated answers for inspection.

Demo

  • FineTune_QWEN3_UM_Handbook_optimized_1.ipynb
    Main notebook that contains the fine-tuning workflow and the TensorTalk HTML chat demo.

Model Artifact Notes

This project may contain several model-related outputs. They are not all used in the same way.

merged_model/

This is the most important deployment artifact for the current demo.

Use this when:

  • running the current TensorTalk HTML chat UI
  • loading the fine-tuned model directly with Hugging Face from_pretrained(...)
  • sharing the main inference-ready model

lora_adapter/

This contains LoRA delta weights only.

Use this when:

  • loading the adapter on top of the original base model
  • reusing the fine-tuning result in a PEFT workflow
  • experimenting with a smaller transferable fine-tuning artifact

.pt exported model file

If present, the .pt file is mainly a saved full-model artifact / backup export.

Use this when:

  • archiving the full fine-tuned weights
  • running a custom loading workflow that explicitly expects a .pt file

For the current TensorTalk chat UI, the primary runtime artifact is still merged_model/.

Current Demo Behavior

The current demo is designed to answer questions such as:

  • dress code and appearance guidance
  • programme core courses / credit requirements
  • undergraduate vs postgraduate handbook information
  • academic rules and handbook-supported policy questions

The answer style is intended to be:

  • handbook-grounded
  • short and direct
  • student-facing
  • non-speculative

Example Demo Output

The screenshot below shows the current TensorTalk chat interface running with the fine-tuned UM handbook model.

TensorTalk Demo

Repository Preview

The screenshot below shows the current top-level project layout.

Repository Structure

Suggested Minimal Deployment Package

If the goal is only to demonstrate the chat UI to teammates, the minimal useful set is:

  • merged_model/
  • the chat notebook / UI code
  • optional avatar image under assets/

The following items are not required for a simple demo run:

  • intermediate training checkpoints
  • test evaluation run directories
  • optional full .pt export
  • raw training logs not used by the demo

Notes

  • The project is organized so that Dataset, models / outputs, and demo code remain separate.
  • The current demo is notebook-friendly and was prepared around a DICC workflow.
  • The deployment path prioritizes clarity and reproducibility over a heavyweight full-stack application setup.

Status

Current project status:

  • handbook preprocessing pipeline prepared
  • supervised QA dataset prepared
  • LoRA fine-tuning workflow completed
  • merged model exported
  • TensorTalk HTML chat demo running
  • evaluation outputs generated

Author / Project Name

TensorTalk
UM Handbook QA / Fine-Tuned Qwen3-8B LoRA Project