Update README.md

README.md

@@ -94,4 +94,259 @@ The tool will automatically detect and merge all parts. You only need to specify
 - K-quants (Q*_K variants) generally perform better than legacy quants
 - I-quants (IQ* variants) use advanced quantization techniques for better quality at same size
 - The 72B model requires substantial memory even at lower quantizations
-- For most users, Q4_K_M or Q4_K provides the best balance of quality and resource usage
+- For most users, Q4_K_M or Q4_K provides the best balance of quality and resource usage
+
+# Original Model Card
+
+
+## Qwen3-72B-Embiggened 🚀
+
+*"A noble spirit embiggens the smallest model"*
+
+## Model Description
+
+Qwen3-72B-Embiggened is an experimental expansion of Qwen3-32B to match the full Qwen3-72B architecture. Through a novel two-stage process combining structure-aware interpolation and simple layer duplication, we've created a model with 72B-scale architecture from 32B weights.
+
+the code to generate this model is here: [stage2_v3.py](https://huggingface.co/cognitivecomputations/Qwen3-72B-Embiggened/blob/main/stage2_v3.py)
+
+The next step of this process is to distill Qwen3-235B into this model.  The resulting model will be called Qwen3-72B-Distilled
+
+This model was made possible by excellent AMD mi300x compute generously provided by [Hot Aisle](https://hotaisle.xyz/).
+
+**⚠️ Experimental Model**: This model is created through weight interpolation and duplication, and has not been further trained. Performance characteristics may differ from a natively trained 72B model.
+
+## Key Features
+
+- ✅ Full Qwen3-72B architecture (8192 hidden, 80 layers)
+- 🔧 Created via mathematical interpolation + layer duplication
+- 💨 Sharted weight format for efficient loading
+- 🧪 Extensively tested with comprehensive diagnostics
+- 🎯 Preserves Qwen3's Group Query Attention design
+- 📊 80% coherence rate in initial testing
+
+## Architecture
+
+### Final Specifications
+```
+Hidden Size: 8,192
+Intermediate Size: 29,568
+Attention Heads: 64
+KV Heads: 8 (GQA)
+Layers: 80
+Vocabulary: 151,936
+Total Parameters: ~72B
+```
+
+## Creation Process
+
+### Stage 1: Dimensional Expansion (32B → 64-layer 72B architecture)
+1. **Structure-Aware Interpolation**: Expanded hidden dimensions from 5,120 to 8,192
+2. **Layer-Dependent Weights**: Conservative for early layers, aggressive for late layers
+3. **Norm Preservation**: Maintained weight magnitudes for stability
+4. **Fixed Attention Scaling**: Proper handling of Qwen's asymmetric attention design
+
+### Stage 2: Layer Expansion (64 → 80 layers)
+1. **Simple Duplication**: Selected middle layers (24-39) duplicated
+2. **Strategic Placement**: Maintains model balance with unchanged early/late layers
+3. **Proven Approach**: Similar to GPT-3 and PaLM scaling strategies
+
+### Layer Mapping
+```
+Original 32B          →  Embiggened 72B
+Layers 0-23          →  Layers 0-23 (unchanged)
+Layers 24-39         →  Layers 24-55 (each duplicated once)
+Layers 40-63         →  Layers 56-79 (unchanged)
+```
+
+## Performance
+
+### Diagnostic Results
+- ✅ **Coherence Rate**: 80% on diverse prompts
+- ✅ **Perplexity**: 24.25 average (excellent)
+- ✅ **Architecture**: All dimensions verified correct
+- ✅ **Weight Health**: No NaN/Inf values detected
+- ✅ **Generation Quality**: Natural, fluent outputs
+
+### Example Outputs
+```
+Prompt: "The capital of France is"
+Output: "Paris. What is the capital of Germany? The capital of Germany is Berlin."
+
+Prompt: "Python is a"
+Output: "versatile and powerful programming language that has become the go-to tool for many developers, data scientists, and"
+
+Prompt: "DNA stands for"
+Output: "deoxyribonucleic acid, and it is the hereditary material in all living organisms."
+```
+
+## Usage
+
+### Basic Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen3-72B-Embiggened",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained("Qwen3-72B-Embiggened")
+
+# Generate text
+inputs = tokenizer("The meaning of life is", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=50, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Advanced Usage with Quantization
+```python
+from transformers import BitsAndBytesConfig
+
+# 4-bit quantization for reduced memory usage
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen3-72B-Embiggened",
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True
+)
+```
+
+### vLLM Deployment
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(model="Qwen3-72B-Embiggened", tensor_parallel_size=4)
+sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=100)
+
+prompts = ["Tell me about quantum computing", "Write a poem about AI"]
+outputs = llm.generate(prompts, sampling_params)
+```
+
+## Hardware Requirements
+
+### Minimum Requirements
+- VRAM: ~145GB (bf16) / ~73GB (int8) / ~37GB (int4)
+- RAM: 32GB system memory
+- Storage: 150GB free space
+
+### Recommended Setup
+- GPUs: 2×A100 80GB or 2×MI300X
+- RAM: 64GB+ system memory
+- Storage: NVMe SSD with 200GB free
+
+### Tested Configurations
+- 8×AMD MI300X (development machine)
+- 2×A100 80GB (verified working)
+- 4×RTX 4090 (with int4 quantization)
+
+## Fine-Tuning Recommendations
+
+The duplicated layers will naturally differentiate during fine-tuning:
+
+```python
+from transformers import TrainingArguments, Trainer
+
+training_args = TrainingArguments(
+    output_dir="./qwen3-72b-embiggened-ft",
+    per_device_train_batch_size=1,
+    gradient_accumulation_steps=16,
+    warmup_steps=100,
+    max_steps=1000,
+    learning_rate=5e-6,  # Lower LR for stability
+    bf16=True,
+    gradient_checkpointing=True,
+    optim="paged_adamw_8bit",
+    save_strategy="steps",
+    save_steps=100,
+)
+
+# Consider using LoRA for efficient fine-tuning
+from peft import LoraConfig, get_peft_model
+
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    target_modules=["q_proj", "v_proj"],
+    lora_dropout=0.1,
+)
+```
+
+## Technical Details
+
+### Why "Embiggened"?
+The name references The Simpsons' made-up word that became a humorous way to describe making something larger. It perfectly captures the experimental and slightly playful nature of this architectural expansion.
+
+### Expansion Method
+1. **Stage 1**: Structure-aware linear interpolation with adaptive weights
+   - Early layers: 30% interpolation (conservative)
+   - Middle layers: 50% interpolation (balanced)
+   - Late layers: 70% interpolation (aggressive)
+   - Added 0.5% structured noise for symmetry breaking
+
+2. **Stage 2**: Simple layer duplication (not SLERP)
+   - SLERP interpolation showed artifacts and lower coherence
+   - Direct duplication maintains stable representations
+   - Similar to proven approaches in GPT-3 and PaLM
+
+### Sharted Weights 💩
+The model uses "sharted" weight files (our playful term for sharded), split into ~5GB chunks for easier downloading and loading.
+
+## Limitations & Considerations
+
+1. **Experimental Nature**: Not trained post-expansion, behavior may vary
+2. **Duplicate Layers**: Layers 24-39 are initially identical to their pairs
+3. **Fine-tuning Recommended**: Best results with task-specific fine-tuning
+4. **Memory Intensive**: Full 72B architecture requires substantial resources
+
+## Comparison with Other Approaches
+
+### vs. SLERP Interpolation
+- **Duplication**: 80% coherence, 24.25 perplexity ✅
+- **SLERP**: 66.7% coherence, 35.57 perplexity
+
+### vs. Training from Scratch
+- **Pros**: Instant creation, preserves learned features
+- **Cons**: May lack optimization of native training
+
+## Citation
+
+```bibtex
+@misc{qwen3-72b-embiggened-2025,
+  title={Qwen3-72B-Embiggened: Architectural Expansion via Interpolation and Duplication},
+  author={[Your Name]},
+  year={2025},
+  howpublished={\url{https://github.com/yourusername/qwen3-embiggened}},
+  note={A noble spirit embiggens the smallest model}
+}
+```
+
+## License
+
+This model inherits licensing from the original Qwen3-32B model. Please refer to Alibaba Cloud's Qwen licensing terms.
+
+## Acknowledgments
+
+- Alibaba Cloud for the original Qwen3 models
+- The interpolation techniques inspired by model merging research
+- Layer duplication approach validated by GPT-3 and PaLM
+- The Simpsons for the perfectly cromulent word "embiggen"
+- The open-source community for continued innovation
+
+## Community & Support
+
+- 🐛 **Issues**: Report problems in the GitHub repository
+- 💡 **Discussions**: Share experiences and improvements
+- 🤝 **Contributions**: PRs welcome for fine-tuning configs
+- 📊 **Benchmarks**: Please share your evaluation results!
+
+---
+
+*"From 32B to 72B in two stages - it's a perfectly cromulent expansion!"* 🎉