dexmac
/

progressive-cognitive-baseline-lora

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 ---
+language: it
+license: apache-2.0
+library_name: peft
+base_model: Qwen/Qwen2.5-1.5B
+tags:
+  - lora
+  - baseline
+  - flat-training
+  - math
+  - arithmetic
+  - control-group
+datasets:
+  - custom
+pipeline_tag: text-generation
 ---
+# Flat-LoRA Baseline (Qwen 2.5 1.5B) — Control Group
+**Baseline (control group) for the Progressive Cognitive Architecture experiment.**
+## What is this?
+This is a standard LoRA adapter trained on the **same 6,000 math examples** as the Progressive model, but:
+- ❌ No 4-phase curriculum (all data mixed together)
+- ❌ No Dream Pruning
+- ❌ No progressive complexity
+This serves as the **control group** to demonstrate that the improvements come from the cognitive architecture, not simply from LoRA fine-tuning.
+## 📊 Results
+| Metric | Flat-LoRA (this) | Dream-LoRA | Base |
+|--------|-----------------|-----------|------|
+| Exact Accuracy | **60.6%** ± 3.8 | 58.6% ± 2.9 | 18.2% |
+| Number Sense | **0.0%** ❌ | 60.0% | 57.0% |
+| Metacognition | **0.0%** ❌ | 100.0% | 84.9% |
+**The Paradox of Accuracy**: Flat-LoRA achieves the highest raw accuracy but completely destroys the model's number sense and ability to delegate. It's an "idiot savant" — good at one thing, bad at everything else.
+## 🚀 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")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B")
+model = PeftModel.from_pretrained(base_model, "dexmac/progressive-cognitive-baseline-lora")
+inputs = tokenizer("Calculate: 347 + 891 =", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=20)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## ⚙️ Training Details
+- **Base model**: Qwen/Qwen2.5-1.5B
+- **LoRA config**: rank=16, alpha=32, targets=q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Training**: 3 epochs, 6,000 mixed samples, lr=1e-4
+- **Hardware**: NVIDIA T4
+## 📄 Related
+- **Progressive model (Dream-LoRA)**: [dexmac/progressive-cognitive-dream-lora](https://huggingface.co/dexmac/progressive-cognitive-dream-lora)
+- **GitHub**: [dexmac221/progressive-cognitive](https://github.com/dexmac221/progressive-cognitive)
+## 📜 License
+Apache 2.0