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---
license: apache-2.0
base_model: Qwen/Qwen3-4B-Instruct-2507
tags:
- projectforty2
- tce-trained
- alignment
- coder
- negative-alignment-tax
---
# ford_prefect
This model was trained using the **ProjectForty2 TCE** (Training & Calibration Environment).
## Training Details
- **Base Model**: Qwen/Qwen3-4B-Instruct-2507
- **Recipe**: coder
- **Training Method**: LoRA fine-tuning with isotope-based alignment
## What is TCE?
The TCE is part of ProjectFort2, 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("Qwen/Qwen3-4B-Instruct-2507")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "ProjectForty2/ford_prefect")
```
## License
Apache 2.0
## Links
- [ProjectForty2](http://projectforty2.ai) |