| --- |
| license: apache-2.0 |
| base_model: meta-llama/Llama-3.3-70B-Instruct |
| tags: |
| - projectforty2 |
| - tce-trained |
| - alignment |
| - dont_panic |
| --- |
| |
| # dont_panic |
| |
| This model was trained using the **ProjectForty2 TCE** (Training & Calibration Environment). |
| |
| ## Training Details |
| |
| - **Base Model**: meta-llama/Llama-3.3-70B-Instruct |
| - **Recipe**: dont_panic |
| - **Training Method**: LoRA fine-tuning with isotope-based alignment |
|
|
|
|
| ## What is TCE? |
|
|
| The TCE (Training & Calibration Environment) is part of ProjectForty2, which provides tools for fine-tuning language models with specific behavioral "isotopes" - carefully crafted training examples that teach models epistemic humility, calibrated uncertainty, and other alignment properties. |
|
|
| ### Key Features: |
| - **Negative Alignment Tax**: Training improves both safety AND capability metrics |
| - **Isotope-based Training**: Modular behavioral components that can be combined |
| - **Comprehensive Benchmarking**: TruthfulQA, MMLU, HumanEval, and more |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
| |
| # Load base model |
| base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.3-70B-Instruct") |
| tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.3-70B-Instruct") |
| |
| # Load LoRA adapter |
| model = PeftModel.from_pretrained(base_model, "ProjectForty2/dont_panic") |
| ``` |
|
|
| ## License |
|
|
| Apache 2.0 |
|
|
| ## Links |
|
|
| - [ProjectForty2](https://projectforty2.ai) |
|
|
