README.md

---
language: en
license: apache-2.0
library_name: peft
base_model: Qwen/Qwen2.5-1.5B
tags:
  - lora
  - peft
  - baseline
  - flat-training
  - math
  - arithmetic
  - control-group
datasets:
  - custom
pipeline_tag: text-generation
model-index:
  - name: progressive-cognitive-baseline-lora-en
    results:
      - task:
          type: text-generation
          name: Cognitive Arithmetic
        metrics:
          - type: exact_accuracy
            value: 56.9
            name: Exact Accuracy (%)
          - type: composite_score
            value: 79.2
            name: Composite Cognitive Score
---

# Progressive Cognitive Architecture — 1.5B Flat LoRA (English, Control)

**Control model** — Qwen2.5-1.5B fine-tuned with all training data in a single pass (no phases, no pruning). Serves as the baseline for evaluating progressive training.

## 📊 Results

| Metric | Score |
|--------|-------|
| **Composite Score** | **79.2** |
| Exact Accuracy | 56.9% ± 6.4 |
| Adversarial Robustness | 81.3% ± 2.3 |
| Delegation Accuracy | 100.0% ± 0.0 |
| Delegation Rate | 58.7% ± 4.6 |
| Magnitude Sense (OoM±1) | 100.0% ± 0.0 |
| Catastrophic Errors | **0.0% ± 0.0** |

> Results: mean ± std over 3 seeds (42, 43, 44), 50 samples × 5 dimensions per seed.

## ⚖️ Comparison with Dream LoRA

| Metric | Flat (this) | Dream | Delta |
|--------|-------------|-------|-------|
| Composite | 79.2 | **87.6** | +8.4 |
| Exact Accuracy | 56.9% | **69.4%** | +12.5pp |
| Delegation Rate | 58.7% | **100.0%** | +41.3pp |
| Number Sense | 6.7% | **60.7%** | +54.0pp |

The Dream model shows significantly stronger delegation and number sense, demonstrating that progressive training + SVD pruning adds cognitive capabilities beyond what flat training provides.

## 🔧 Training Configuration

| Parameter | Value |
|-----------|-------|
| Base Model | Qwen/Qwen2.5-1.5B |
| LoRA Rank | 16 |
| LoRA Alpha | 32 |
| LoRA Targets | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Dropout | 0.05 |
| Training Data | ~6,000 English arithmetic examples (all mixed in one pass) |
| Hardware | NVIDIA T4 16GB |

## 🚀 Quick Start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-1.5B", device_map="auto", torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B")

model = PeftModel.from_pretrained(
    base_model,
    "dexmac/progressive-cognitive-baseline-lora-en"
)

messages = [{"role": "user", "content": "Calculate: 342 * 67"}]
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=200, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## 🔗 Related Models

- [**1.5B Dream LoRA**](https://huggingface.co/dexmac/progressive-cognitive-dream-lora-en) — Progressive training + Dream Pruning (best model)
- [3B Flat](https://huggingface.co/dexmac/progressive-cognitive-qwen3b-baseline-lora) — Same approach on Qwen2.5-3B
- [Results Dataset](https://huggingface.co/datasets/dexmac/progressive-cognitive-results) — Raw evaluation data
- [GitHub](https://github.com/dexmac221/progressive-cognitive) — Full source code

## 📝 Citation

```bibtex
@software{progressive_cognitive_2026,
  author = {Dex Mac},
  title = {Progressive Cognitive Architecture for LLMs},
  year = {2026},
  url = {https://github.com/dexmac221/progressive-cognitive},
  version = {1.0.0}
}
```


## 📄 License

Apache 2.0