---
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)