You can deploy this model and start earning money today!

build-small-hackathon/
compliment-forest-minicpm5-1b

Name: compliment-forest-minicpm5-1b
Availability: InStock

Views

No views yet

Compliment Forest MiniCPM5-1B

This is the merged text fine-tune for The Compliment Forest. It turns a name and situation into schema-valid, situation-grounded forest JSON for the app's author pass. The same model also runs a bounded critic pass that prunes generic or redundant clearings.

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.

build-small-hackathon/compliment-forest-minicpm5-1b

Compliment Forest MiniCPM5-1B

Training

Inference

License

build-small-hackathon/
compliment-forest-minicpm5-1b