54 lines
1.6 KiB
Markdown
54 lines
1.6 KiB
Markdown
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---
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base_model: openbmb/MiniCPM5-1B
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library_name: transformers
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- minicpm
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- openbmb
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- qlora
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- llama.cpp
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- structured-generation
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- build-small-hackathon
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datasets:
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- build-small-hackathon/compliment-forest-sft
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---
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# Compliment Forest MiniCPM5-1B
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This is the merged text fine-tune for
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[The Compliment Forest](https://huggingface.co/spaces/build-small-hackathon/compliment-forest).
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It turns a name and situation into schema-valid, situation-grounded forest JSON for the app's
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author pass. The same model also runs a bounded critic pass that prunes generic or redundant
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clearings.
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## Training
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- Base: `openbmb/MiniCPM5-1B` (Llama architecture, about 1.08B parameters)
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- Data: `build-small-hackathon/compliment-forest-sft`
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- Method: 4-bit NF4 QLoRA on Modal
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- LoRA: rank 16, alpha 32, dropout 0.05
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- Targets: attention and MLP projections
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- Sequence length: 2,048
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- Epochs: 2
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- Learning rate: 2e-4 with cosine decay
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- Runtime thinking mode: disabled for deterministic JSON generation
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The dataset was filtered for JSON validity, concrete situation grounding, non-toxic positivity,
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and short first-person spells. This model is for whimsical encouragement; it is not a therapist
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or a substitute for professional support.
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## Inference
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Use the base model's chat template with `enable_thinking=False`. The app enforces the output with
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Pydantic and retries malformed generations at most twice.
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The repository also includes a `Q4_K_M` GGUF build for local `llama.cpp` inference.
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## License
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Apache-2.0, following the base model and project code. Dataset source licenses are documented on
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the dataset card.
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