---
language: it
license: apache-2.0
library_name: peft
base_model: Qwen/Qwen2.5-1.5B
tags:
  - lora
  - peft
  - cognitive-architecture
  - progressive-learning
  - dream-pruning
  - svd
  - math
  - arithmetic
  - intuition
  - tool-use
datasets:
  - custom
pipeline_tag: text-generation
---

# Architettura Cognitiva Progressiva — Dream-LoRA con SVD Pruning (Italiano)

**Modello principale italiano** — Qwen2.5-1.5B addestrato con architettura cognitiva progressiva a 4 fasi + **SVD Dream Pruning** (rank 16→8).

## 📊 Risultati

| Metrica | Dream-LoRA (questo) | Progressive-LoRA | Flat-LoRA |
|---------|---------------------|------------------|-----------|
| Accuratezza Esatta | **58.6% ± 2.9** | 37.0% ± 0.5 | 60.6% |
| Number Sense | **60.0% ± 0.8** | 57.7% ± 0.5 | 0.0% |
| Metacognizione | **100.0%** | 98.5% | 0.0% |

Il passaggio da magnitude pruning a SVD Dream Pruning ha migliorato significativamente l'accuratezza esatta (+21.6pp) preservando number sense e metacognizione.

## 🧠 Progressive Cognitive Architecture

A bio-inspired 4-phase training methodology:

| Phase | Name | What happens |
|-------|------|-------------|
| 1 | **Foundation** | Learn exact arithmetic via LoRA fine-tuning |
| 2 | **Consolidation** | SVD Dream Pruning (rank 16→8) compresses knowledge into intuition |
| 3 | **Delegation** | Learn complexity-aware routing: compute internally vs. delegate to tool |
| 4 | **Orchestration** | Full pipeline: intuit → route → tool → validate |

**Guiding Principle:** *Knowledge doesn't disappear — it collapses into attractors. Intuition is the compressed residue of experience.*


## 🌙 Dream Pruning (Fattorizzazione SVD a Basso Rango)

Invece di azzerare i pesi piccoli, il Dream Pruning usa la **decomposizione SVD** per ridurre il rango effettivo delle matrici LoRA da 16 a 8. Preserva le direzioni principali ("connessioni logiche") scartando il rumore — analogo al consolidamento della memoria durante il sonno.

## 🔧 Configurazione

| Parametro | Valore |
|-----------|--------|
| Modello Base | Qwen/Qwen2.5-1.5B |
| LoRA Rank | 16 (→ 8 dopo SVD) |
| LoRA Alpha | 32 |
| Target LoRA | q_proj, k_proj, v_proj, o_proj |
| Tipo Pruning | SVD Low-Rank Factorization |
| Lingua Dati | Italiano |

## 🚀 Uso Rapido

```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-dream-lora",
    subfolder="lora_adapters"
)

messages = [{"role": "user", "content": "Risolvi: 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))
```

## 🔗 Modelli Correlati

- [Progressive-LoRA (IT)](https://huggingface.co/dexmac/progressive-cognitive-lora) — Primo prototipo con magnitude pruning
- [Flat-LoRA (IT)](https://huggingface.co/dexmac/progressive-cognitive-baseline-lora) — Controllo senza fasi
- [**1.5B Dream (EN)**](https://huggingface.co/dexmac/progressive-cognitive-dream-lora-en) — Miglior modello (inglese, composite 87.6)
- [GitHub](https://github.com/dexmac221/progressive-cognitive) — Codice sorgente completo

## 📝 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