1.6 KiB
base_model, library_name, license, language, pipeline_tag, tags, datasets
| base_model | library_name | license | language | pipeline_tag | tags | datasets | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| openbmb/MiniCPM5-1B | transformers | apache-2.0 |
|
text-generation |
|
|
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.