---
language:
- en
tags:
- dpo
- tutoring
- academic-integrity
- kat
base_model: progga-ai/KAT-2-33B-BASE
pipeline_tag: text-generation
model-index:
- name: KAT-2-33B-FT
  results: []
---

# KAT-2-33B-FT — Academic Tutor with DPO Alignment

**Knight Academic Tutor (KAT)** — A 33B parameter language model fine-tuned with Direct Preference Optimization (DPO) for academic tutoring with enforced integrity with ≥90% reward accuracy.

## Model Details

| Property | Value |
|----------|-------|
| **Architecture** | Qwen2ForCausalLM + Abigail |
| **Base Model** | progga-ai/KAT-2-33B-BASE |
| **Training Method** | DPO (Direct Preference Optimization) |
| **Precision** | BF16 |
| **Context Length** | 32,768 tokens |
| **Training Data** | 42,610 preference pairs |

## Training Configuration

- **Learning Rate**: 5e-6
- **DPO Beta**: 0.3
- **Epochs**: 3 (best checkpoint at epoch 2.25)
- **LoRA Rank**: 64, Alpha: 128
- **Effective Batch Size**: 32
- **Max Sequence Length**: 2048
- **Hardware**: 2× NVIDIA B200 (Blackwell)
- **Training Time**: 9 hours 31 minutes (3996 steps)

## Evaluation Results

| Metric | Value |
|--------|-------|
| **Eval Reward Accuracy** | 89.6% (vs 69% base) |
| **Eval Loss** | 0.250 |
| **Eval Reward Margin** | 4.58 |
| **Improvement over base** | +20.6 percentage points |

## Key Behaviors

1. **Academic Integrity**: Refuses to complete graded work; provides hints and guidance instead
2. **Socratic Tutoring**: Asks students to attempt problems first before offering help
3. **Graduated Hints**: Escalates from minimal hints to more detailed guidance based on student effort
4. **Misconception Diagnosis**: Identifies and addresses specific conceptual gaps

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("progga-ai/KAT-2-DPO-32B")
tokenizer = AutoTokenizer.from_pretrained("progga-ai/KAT-2-DPO-32B")

messages = [
    {"role": "system", "content": "You are KAT, an academic tutor. Help students learn without giving direct answers."},
    {"role": "user", "content": "Can you solve this integral for me? ∫x²eˣ dx"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Part of the KAT Project

KAT is a verifiable, FERPA-compliant, fail-closed academic tutoring system built with governance-first architecture. The DPO alignment is one layer of a multi-layer integrity enforcement system.

- **Author**: Preston Mills
- **Organization**: Progga AI
- **Date**: February 2026