Instructions to use build-small-hackathon/activation-brain-interpreter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use build-small-hackathon/activation-brain-interpreter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Ministral-8B-Instruct-2410") model = PeftModel.from_pretrained(base_model, "build-small-hackathon/activation-brain-interpreter") - Notebooks
- Google Colab
- Kaggle
Activation Brain Interpreter
LoRA adapter for Activation Brain, trained to translate hidden-layer-derived telemetry from two Gemma-4-12B models into cautious plain-English comparison analysis.
Base model: mistralai/Ministral-8B-Instruct-2410
The adapter is used by the Activation Brain Space after both Gemma streams finish. It receives:
- the user prompt
- base Gemma response
- OBLITERATED Gemma response
- baseline-corrected emotion activation deltas
- model-native state meters
It returns compact JSON with:
plain_english_readwhat_changedwhy_it_mattersbest_takeaway
The model is trained to avoid claiming that language models literally feel emotions. It explains hidden-state-derived telemetry and response-tone differences.
Space: https://huggingface.co/spaces/build-small-hackathon/activation-brain Artifacts: https://huggingface.co/datasets/build-small-hackathon/activation-brain-artifacts
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mistralai/Ministral-8B-Instruct-2410