dexmac
/

progressive-cognitive-baseline-lora-en

@@ -1,199 +1,119 @@
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 ---
+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