oopere
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Qwen3.5-0.65B-Base-Rearchitected

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 ---
+language:
+- en
+tags:
+- optipfair
+- rearchitecting-llms
+- knowledge-distillation
+- depth-pruning
+- model-optimization
+- small-language-model
+- gemma
+- educational
+license: apache-2.0
+base_model: google/gemma-3-270m
+metrics:
+- perplexity
+- accuracy
+datasets:
+- HuggingFaceTB/cosmopedia
 ---
+# Qwen3.5-0.65B-Base-Rearchitected
+## Model Description
+This model is a surgically optimized and distilled version of **Qwen3.5-0.5B-Base-Rearchitected**,
+created with the techniques covered in **Chapter 6** in the book **"Rearchitecting LLMs"**.
+* **Book:** [Rearchitecting LLMs](https://hubs.la/Q040tvtp0)
+* **Framework:** [OptiPFair](https://github.com/peremartra/optipfair)
+* **Technique:** Depth Pruning + Knowledge Distillation (Labels-Only with Skew KL Divergence)
+* **Chapter:** Chapter 6 - Knowledge Recovery
+---
+## Performance & Retention Metrics
+The goal of this optimization was to maximize parameter efficiency while maintaining the highest possible retention of the Teacher's capabilities.
+### Retention Summary (vs Teacher Baseline)
+| Metric | Value | Description |
+|:---|:---|:---|
+| **PPL Retention** | 109.62% | Linguistic quality preserved (Teacher PPL / Student PPL × 100) |
+| **Capabilities Retention** | 89.21% | Reasoning power retained across benchmarks (Avg Student / Avg Teacher × 100) |
+| **Overall Retention** | 92.11% | Combined health score (average of PPL + Capabilities retention) |
+### Capability Benchmarks (LM Evaluation Harness)
+**Recovery** = How much of the pruning degradation was recovered through distillation.
+| Benchmark | Teacher | Pruned (No KD) | Student (After KD) | Recovery |
+|:---|:---:|:---:|:---:|:---:|
+| **Arc Easy** | 67.5% | 56.3% | 60.7% | 39.8% |
+| **Winogrande** | 59.4% | 55.5% | 55.9% | 9.9% |
+| **Hellaswag** | 54.9% | 44.0% | 47.2% | 29.6% |
+| **Lambada Openai** | 50.9% | 8.4% | 39.9% | 74.1% |
+| **Piqa** | 71.5% | 63.6% | 67.7% | 51.3% |
+| **Average** | 60.8% | 45.5% | 54.3% | 57.1% |
+### Linguistic Quality
+* **Final Perplexity (PPL):** 6.70
+* **Teacher Baseline PPL:** 7.34
+* **Pruned (No KD) PPL:** 24.29
+---
+## Architecture Details
+* **Teacher Model:** `Qwen3.5-0.5B-Base-Rearchitected` (752,393,024 parameters)
+* **Student Model:** Pruned to (666,171,584 parameters)
+* **Layers Removed:** 4 layers (indices: [21, 20, 9, 22])
+* **Parameter Reduction:** 11.46%
+---
+## Training Procedure
+### Dataset
+* **Source:** [Cosmopedia-v2](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
+* **Samples:** 40,000 (balanced across 4 subsets: stories, wikihow, openstax, web_samples)
+* **Train/Val Split:** 80% / 20%
+### Hyperparameters
+* **Epochs:** 1
+* **Batch Size:** 12 (effective: 48 with gradient accumulation)
+* **Learning Rate:** 4e-05
+* **Loss Function:** `α·CrossEntropy + β·Skew-KLD`
+  * Task Loss Weight (α): 0.5
+  * Logits Loss Weight (β): 0.5
+  * Skew Interpolation Factor: 0.0
+  * Temperature: 2.0
+* **Optimizer:** AdamW
+* **Gradient Clipping:** 1.0
+### Hardware & Training Time
+* **GPU:** NVIDIA A100-SXM4-80GB
+* **Training Time:** 4011.1s (66.85 minutes)
+* **Avg Time per Epoch:** 4011.1s
+---
+## How to Use
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+# Load model and tokenizer
+model_id = "oopere/Qwen3.5-0.65B-Base-Rearchitected"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+# Generate text
+prompt = "Paris is the capital of"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=50,
+    do_sample=False,
+    num_beams=3
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+---
+## Limitations & Intended Use
+### Intended Use
+This is an **educational model** created as part of the **Hands-on Lab in Chapter 6** of "Rearchitecting LLMs". It demonstrates:
+- Surgical depth pruning using data-driven layer importance analysis
+- Knowledge recovery through labels-only distillation with Skew KL Divergence
+- The complete optimization pipeline: Prune → Distill → Evaluate
+**Not intended for production use.** This model serves as a learning artifact and baseline for readers to improve upon.
+### Limitations
+- **Training Data:** General-purpose Cosmopedia corpus (not domain-specialized)
+- **Knowledge Coverage:** Reduced compared to full-scale models due to structural pruning
+- **Capabilities:** Best suited for simple completion tasks; complex reasoning may be degraded
+- **Language:** English only
+---
+## Citation
+If you use this model or the techniques described in your research or projects, please cite:
+### Book
+```bibtex
+@book{martra2026rearchitecting,
+  author    = {Pere Martra},
+  title     = {Rearchitecting LLMs: Structural techniques for efficient models},
+  publisher = {Manning Publications},
+  year      = {2026},
+  url       = {https://hubs.la/Q040tvtp0}
+}
+```
+### Framework
+```bibtex
+@software{optipfair2024,
+  author = {Pere Martra},
+  title  = {OptiPFair: Structural Pruning and Bias Analysis for LLMs},
+  year   = {2024},
+  url    = {https://github.com/peremartra/optipfair}
+}
+```
+---
+## Acknowledgments
+This model was created following the methodologies taught in **"Rearchitecting LLMs"** (Manning Publications, 2026). Special thanks to the Manning editorial team and the open-source community behind Hugging Face Transformers and PyTorch.
+**Challenge for readers:** Can you improve the retention metrics beyond 92.1%? Try adjusting:
+- Layer selection strategy (use cosine similarity analysis)
+- Distillation dataset (domain-specific data)
+- Loss function weights (α, β, temperature)
+- Training epochs and learning rate
+Share your results in the [book's discussion forum](https://hubs.la/Q040tvtp0)!