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

ProjectForty2/dont_panic

dont_panic

Training Details

What is TCE?

Key Features:

Usage

License

Links

ProjectForty2/
dont_panic