llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)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
- Downloads last month
- 651
Hardware compatibility
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Base model
Qwen/Qwen2.5-VL-3B-Instruct
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="nikowru/koalipi-slm", filename="", )