README.md

metadata

base_model: Qwen/Qwen2.5-VL-3B-Instruct
language:
  - fil
  - en
license: apache-2.0
tags:
  - qwen2.5-vl
  - education
  - filipino
  - fine-tuned
  - gguf

KoaliPi SLM — Qwen2.5-VL-3B Fine-tune

KoaliPi SLM is a fine-tuned vision-language model built on top of Qwen2.5-VL-3B-Instruct, trained for AI-powered STEM study assistance for Filipino students (high school and college level).

Files

File	Size	Description
koalipi-slm-q4km.gguf	~1.9 GB	Q4_K_M quantized — recommended for on-device use
koalipi-slm.gguf	3.29 GB	Q8_0 quantized — higher precision

Training Details

• Base model: Qwen/Qwen2.5-VL-3B-Instruct
• Method: LoRA fine-tuning via Unsloth
• Training steps: 30
• Quantization: 16-bit during training, exported to Q4_K_M via llama.cpp

Training Data

The model was fine-tuned on a custom KoaliPi dataset covering:

• Handwriting parsing (IAM handwriting dataset + custom Filipino notes)
• Filipino Q&A (Taglish STEM explanations)
• English Q&A (STEM concepts)
• MCQ generation
• Practice problem generation
• Study plan generation

Instruction Format

No system prompt needed. Uses ChatML format:

Document parsing:

Parse this document. Return ONLY a valid JSON with these fields:

topics, difficulty, subject, key_concepts, has_equations, equations, summary.

No explanation, just JSON.

MCQ generation: Generate 3 multiple choice questions about {topic}.

Return ONLY valid JSON array.

Filipino Q&A: {question in Filipino or English}

Intended Use

• Filipino high school and college STEM students
• On-device inference via llama.rn
• Part of the KoaliPi AI study app

Limitations

• mmproj (vision encoder) not yet exportable via llama.cpp for Qwen2.5-VL
• Image/VL features require mmproj — text features fully functional
• 30 training steps — suitable for demo, expand dataset for production