Training
- Base:
openbmb/MiniCPM5-1B (Llama architecture, about 1.08B parameters)
- Data:
build-small-hackathon/compliment-forest-sft
- Method: 4-bit NF4 QLoRA on Modal
- LoRA: rank 16, alpha 32, dropout 0.05
- Targets: attention and MLP projections
- Sequence length: 2,048
- Epochs: 2
- Learning rate: 2e-4 with cosine decay
- Runtime thinking mode: disabled for deterministic JSON generation
The dataset was filtered for JSON validity, concrete situation grounding, non-toxic positivity,
and short first-person spells. This model is for whimsical encouragement; it is not a therapist
or a substitute for professional support.
Inference
Use the base model's chat template with enable_thinking=False. The app enforces the output with
Pydantic and retries malformed generations at most twice.
The repository also includes a Q4_K_M GGUF build for local llama.cpp inference.
License
Apache-2.0, following the base model and project code. Dataset source licenses are documented on
the dataset card.