MyVillage Activity Generation v1.3.5 — Qwen2.5-0.5B — 325 Checkpoint
Fine-tuned from Qwen/Qwen2.5-0.5B-Instruct for MyVillage next-activity generation.
Training
- Training examples: 325
- Training method: supervised fine-tuning with QLoRA
- Starting checkpoint: clean
Qwen/Qwen2.5-0.5B-Instruct - Epochs: 4
- Learning rate: 0.0001
- LoRA rank: 16
- Target context length: 1024
The repository name uses 325 as the experiment/checkpoint label. The validated training dataset used by this notebook contains 325 examples.
Input
The model expects:
- village goal
- previous activity title
- knowledge submission
- one wisdom object containing book name, book type, chapter title, and content
Output
The model is trained to return JSON with:
{
"title": "...",
"description": "...",
"instructions": "..."
}
Intended behavior
The knowledge submission is treated as the strongest evidence of the learner's current state.
The model is trained to avoid repeating work already demonstrated, handle vague submissions conservatively, avoid using the word but as a shortcut, and use the village goal as long-term context rather than blindly forcing the immediate activity toward it.
Important limitation
This is an experimental small-model checkpoint trained on a small synthetic dataset. Behavioral quality should be evaluated on held-out MyVillage scenarios before production use.