---
base_model: openbmb/MiniCPM5-1B
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- minicpm
- openbmb
- qlora
- llama.cpp
- structured-generation
- build-small-hackathon
datasets:
- build-small-hackathon/compliment-forest-sft
---

# Compliment Forest MiniCPM5-1B

This is the merged text fine-tune for
[The Compliment Forest](https://huggingface.co/spaces/build-small-hackathon/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.