Update README.md

README.md

@@ -14,6 +14,7 @@ tags:
 datasets:
 - Anthropic/hh-rlhf
 - stingning/ultrachat
+- vicgalle/alpaca-gpt4
 metrics:
 - perplexity
 library_name: transformers
@@ -21,13 +22,13 @@ pipeline_tag: text-generation
 base_model: gpt2
 ---
 
-# Recursive Language Model - 90M (Adaptive Computation)
+# Mixture of Recursion Language Model - 198M (Adaptive Computation)
 
-A novel 90M parameter conversational language model featuring **adaptive recursive computation** through perplexity-based dynamic routing. Achieves better performance than GPT-2 Small (117M) with 30% fewer parameters.
+A novel 198M parameter conversational language model featuring **adaptive recursive computation** through perplexity-based dynamic routing. Achieves better performance than GPT-2 Medium (345M) with 57% fewer parameters.
 
 ## 🏆 Key Achievement
 
-**Perplexity: 17.45** - Outperforms GPT-2 Small (29 perplexity) despite being significantly smaller.
+**Perplexity: 15.37** - Outperforms GPT-2 Medium (22 perplexity) despite being significantly smaller.
 
 ## 🔥 Innovation
 
@@ -40,6 +41,7 @@ Instead of applying uniform computation to all inputs, this model features:
 - **Perplexity-Based Router**: Neural classifier that learns sample difficulty from the model's own confidence
 - **Adaptive Recursion**: Dynamically allocates 1, 3, or 5 recursive transformer passes based on input complexity
 - **Self-Supervised Learning**: No manual labels - the model learns what's "hard" from its own perplexity signals
+- **NaN-Safe Attention**: Uses -1e4 masking instead of -inf for stable fp16 training
 
 ### How It Works
 ```
@@ -56,28 +58,31 @@ This enables **intelligent compute allocation** - simple inputs get fast process
 
 | Model | Parameters | Perplexity | Notes |
 |-------|-----------|------------|-------|
-| **This Model** | **90.7M** | **17.45** | **Novel adaptive architecture** |
-| GPT-2 Small | 117M | ~29 | Baseline comparison |
-| GPT-2 Medium | 345M | ~22 | 3.8× larger |
+| **This Model** | **198M** | **15.37** | **Novel adaptive architecture** |
+| GPT-2 Small | 117M | ~29 | Smaller baseline |
+| GPT-2 Medium | 345M | ~22 | 1.7× larger |
+| GPT-2 Large | 774M | ~18 | 3.9× larger |
 | Random Baseline | - | ~50,000 | Theoretical worst |
 
 ### Training Metrics
 ```
 📈 Training Progression:
-Epoch 1: 35.39 perplexity
-Epoch 2: 20.27 perplexity (43% improvement)
-Epoch 3: 17.45 perplexity (51% total improvement)
+Epoch 1: 21.75 perplexity  (Val loss: 3.0798)
+Epoch 2: 15.37 perplexity  (Val loss: 2.7326) ← BEST
 
 📉 Loss Reduction:
-Start: 4.50 → Final: 2.86 (36% reduction)
+Train loss: 4.5081 → 3.3068 (27% reduction)
+Val loss:   3.0798 → 2.7326 (11% reduction)
+nan_batches: 0 (stable training throughout)
 ```
 
 ### Performance Highlights
 
-✅ **17.45 perplexity** on validation set (37K samples)  
-✅ **Better than GPT-2 Small** with 30% fewer parameters  
+✅ **15.37 perplexity** on validation set (2,000 samples)  
+✅ **Better than GPT-2 Medium** with 57% fewer parameters  
 ✅ **Efficient inference** - adaptive computation saves resources  
 ✅ **Novel architecture** - not just fine-tuning  
+✅ **Stable training** - 0 NaN batches across 150K steps  
 
 ## 🎯 Model Architecture
 
@@ -85,13 +90,13 @@ Start: 4.50 → Final: 2.86 (36% reduction)
 
 | Component | Configuration |
 |-----------|--------------|
-| **Total Parameters** | 90,697,603 (~90.7M) |
-| **Vocabulary Size** | 50,259 tokens (GPT-2 BPE + special tokens) |
-| **Embedding Dimension** | 560 |
-| **Base Transformer Layers** | 8 |
-| **Attention Heads** | 8 heads per layer |
-| **Head Dimension** | 70 (560 ÷ 8) |
-| **FFN Intermediate Size** | 2240 |
+| **Total Parameters** | ~198M |
+| **Vocabulary Size** | 50,260 tokens (GPT-2 BPE + special tokens) |
+| **Embedding Dimension** | 768 |
+| **Base Transformer Layers** | 16 |
+| **Attention Heads** | 12 heads per layer |
+| **Head Dimension** | 64 (768 ÷ 12) |
+| **FFN Intermediate Size** | 3072 |
 | **Max Sequence Length** | 512 tokens |
 | **Positional Encoding** | Rotary Positional Embeddings (RoPE) |
 | **Dropout Rate** | 0.1 (hidden & attention) |
@@ -106,29 +111,30 @@ Start: 4.50 → Final: 2.86 (36% reduction)
 
 ### Architecture Components
 
-1. **Token Embedding Layer** (50,259 × 560)
-   - Special tokens: `<|user|>`, `<|assistant|>`, `<|endoftext|>`
+1. **Token Embedding Layer** (50,260 × 768)
+   - Special tokens: `<|user|>`, `<|assistant|>`, `<|pad|>`, `<|endoftext|>`
 
-2. **Base Transformer Stack** (8 layers)
+2. **Base Transformer Stack** (16 layers)
    - Multi-head self-attention with RoPE
-   - Feed-forward networks (560 → 2240 → 560)
+   - Feed-forward networks (768 → 3072 → 768)
    - Pre-normalization with LayerNorm
    - Residual connections
+   - NaN-safe attention masking (-1e4)
 
-3. **Perplexity-Based Router** (~0.4M params)
+3. **Perplexity-Based Router** (~1.2M params)
    - Attention-weighted sequence pooling
-   - 2-layer MLP classifier (560 → 280 → 3)
+   - 2-layer MLP classifier (768 → 384 → 3)
    - Trained on pseudo-labels from sample perplexity
    - Outputs: complexity class (0=simple, 1=medium, 2=complex)
 
-4. **Recursive Refinement Layer** (~3.8M params)
+4. **Recursive Refinement Layer** (~7M params)
    - Transformer block applied 1-5 times adaptively
    - Same architecture as base layers
    - Reused weights for parameter efficiency
 
 5. **Output Projection**
    - Final LayerNorm
-   - Linear layer (560 → 50,259)
+   - Linear layer (768 → 50,260)
 
 ## 🚀 Quick Start
 
@@ -139,120 +145,82 @@ pip install transformers torch
 
 ### Basic Usage
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from mixture_of_recursion import RecursiveLanguageModel
+from transformers import AutoTokenizer
 import torch
 
-# Load model and tokenizer
-model = AutoModelForCausalLM.from_pretrained(
-    "Girinath11/recursive-language-model-90m",
+# Load model
+model = RecursiveLanguageModel.from_pretrained(
+    "Girinath11/recursive-language-model-198m",
     trust_remote_code=True
 )
 tokenizer = AutoTokenizer.from_pretrained(
-    "Girinath11/recursive-language-model-90m"
+    "Girinath11/recursive-language-model-198m",
+    trust_remote_code=True
 )
 
-# Move to GPU if available
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model = model.to(device)
 model.eval()
 
 print(f"✅ Model loaded on {device}")
-print(f"📊 Parameters: {model.num_parameters():,}")
+print(f"📊 Parameters: {sum(p.numel() for p in model.parameters()):,}")
 ```
 
 ### Conversational Format
 ```python
-# Model expects this format
-prompt = """<|user|>
-What is machine learning?
-<|assistant|>
-"""
-
-inputs = tokenizer(prompt, return_tensors="pt").to(device)
-
-# Generate response
-with torch.no_grad():
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=100,
-        temperature=0.7,
-        top_p=0.9,
-        do_sample=True,
-        pad_token_id=tokenizer.eos_token_id
-    )
-
-response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(response)
-```
-
-### Batch Generation
-```python
-prompts = [
-    "<|user|>\nExplain quantum computing simply.\n<|assistant|>\n",
-    "<|user|>\nWrite a haiku about AI.\n<|assistant|>\n",
-    "<|user|>\nHow do neural networks work?\n<|assistant|>\n"
-]
-
-# Tokenize all prompts
-inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(device)
-
-# Generate for all at once
-with torch.no_grad():
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=80,
-        temperature=0.8,
-        top_p=0.9,
-        do_sample=True,
-        pad_token_id=tokenizer.eos_token_id
-    )
-
-# Decode all outputs
-for i, output in enumerate(outputs):
-    text = tokenizer.decode(output, skip_special_tokens=True)
-    print(f"\nPrompt {i+1}:\n{text}\n{'-'*60}")
+def chat(question, max_new_tokens=150, temperature=0.7, top_p=0.9):
+    # Must use chat format — model trained on this
+    prompt = f"<|user|>\n{question}\n<|assistant|>\n"
+
+    inputs = tokenizer(
+        prompt,
+        return_tensors="pt",
+        add_special_tokens=False
+    ).to(device)
+
+    with torch.no_grad():
+        outputs = model.generate(
+            inputs['input_ids'],
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=True,
+        )
+
+    full_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    if "<|assistant|>" in full_text:
+        return full_text.split("<|assistant|>")[-1].strip()
+    return full_text
+
+# Example
+print(chat("What is machine learning?"))
+print(chat("Explain neural networks simply"))
+print(chat("What is Python programming?"))
 ```
 
 ### Generation Parameters
 ```python
 # Creative writing (high temperature)
-creative_output = model.generate(
-    **inputs,
-    max_new_tokens=150,
-    temperature=1.0,      # More random/creative
-    top_p=0.95,
-    top_k=50,
-    do_sample=True
-)
+response = chat("Write a story about AI", temperature=1.0, top_p=0.95)
 
 # Focused/deterministic (low temperature)
-focused_output = model.generate(
-    **inputs,
-    max_new_tokens=100,
-    temperature=0.5,      # More focused
-    top_p=0.9,
-    do_sample=True
-)
-
-# Greedy decoding (most likely tokens)
-greedy_output = model.generate(
-    **inputs,
-    max_new_tokens=50,
-    do_sample=False       # Deterministic
-)
+response = chat("Explain quantum computing", temperature=0.5, top_p=0.9)
 ```
 
 ## 📚 Training Details
 
 ### Dataset
 
-**Total Training Samples: 37,119** (high-quality conversational data)
+**Total Training Samples: 150,000** (high-quality conversational data)
 
-| Dataset | Source | Samples | Percentage | Description |
-|---------|--------|---------|------------|-------------|
-| **Anthropic HH-RLHF** | Anthropic | 30,000 | 81% | Claude-style helpful & harmless responses |
-| **UltraChat** | Tsinghua | 7,119 | 19% | GPT-4 generated multi-turn dialogues |
-| **Validation** | Mixed | 2,000 | - | Held-out samples for evaluation |
+| Dataset | Samples | Percentage | Description |
+|---------|---------|------------|-------------|
+| **Anthropic HH-RLHF** | 80,000 | 53% | Helpful & harmless responses |
+| **UltraChat** | 50,000 | 33% | GPT-4 generated multi-turn dialogues |
+| **Alpaca-GPT4** | 20,000 | 14% | Instruction following data |
+| **Validation** | 2,000 | - | Held-out evaluation samples |
 
 **Data Format:**
 ```
@@ -263,249 +231,151 @@ Assistant response here
 <|endoftext|>
 ```
 
-**Quality Control:**
-- Minimum length: 80 characters
-- Maximum length: 6,000 characters
-- Minimum word count: 15 words
-- Alphanumeric ratio: >65%
-- Required: Proper punctuation
-- Validation: Strict conversation structure
-
 ### Training Configuration
 ```yaml
 Hardware:
-  GPU: NVIDIA Tesla T4 (15.64 GB)
+  GPU: NVIDIA Tesla T4 (15.6 GB)
   Platform: Kaggle
-  Framework: PyTorch + Transformers
+  Framework: PyTorch + HuggingFace Transformers
   Mixed Precision: FP16 (AMP)
 
 Hyperparameters:
-  Batch Size: 8
-  Gradient Accumulation: 8
+  Batch Size: 2
+  Gradient Accumulation: 32
   Effective Batch Size: 64
   Max Sequence Length: 512
-  Learning Rate: 3e-4
-  Optimizer: AdamW
+  Learning Rate: 1e-4
+  Optimizer: AdamW (betas: 0.9, 0.95)
   Weight Decay: 0.01
-  LR Schedule: OneCycleLR
-  Warmup: 10% of total steps
+  LR Schedule: LinearWarmup + CosineAnnealing
+  Warmup Steps: 500
   Gradient Clipping: 1.0
-  Total Epochs: 3
-  Total Steps: 13,917
+  Total Epochs: 2
+  Total Steps: 150,000
 
 Loss Function:
   Language Modeling: CrossEntropyLoss
   Router Loss: CrossEntropyLoss (weight: 0.1)
   Total: LM Loss + 0.1 × Router Loss
 
-Regularization:
-  Hidden Dropout: 0.1
-  Attention Dropout: 0.1
+Key Fix:
+  Attention Mask: -1e4 (NOT -inf) → prevents NaN in fp16
+  Pad Token: Separate from EOS token
 ```
 
-### Training Schedule
-
-- **Steps per Epoch:** 4,639
-- **Training Speed:** ~2.7 it/s
-- **GPU Utilization:** ~85-90%
-- **Memory Usage:** ~8-9 GB (peak)
-
 ### Training Progression
 
-| Epoch | Train Loss | Eval Loss | Perplexity | Status |
-|-------|-----------|-----------|------------|---------|
-| **1** | 4.4979 | 3.5665 | 35.39 | Initial learning |
-| **2** | 3.2960 | 3.0091 | 20.27 | Rapid improvement |
-| **3** | 2.8572 | 2.8595 | **17.45** | 🔥 **BEST!** |
+| Epoch | Train Loss | Val Loss | Perplexity | NaN Batches |
+|-------|-----------|----------|------------|-------------|
+| **1** | 4.5081 | 3.0798 | 21.75 | 0 |
+| **2** | 3.3068 | 2.7326 | **15.37** 🔥 | 0 |
 
 **Key Observations:**
 - Loss decreased steadily across all epochs
-- No overfitting (train/eval losses converged)
-- 51% perplexity improvement from Epoch 1 to 3
-- Stable training with consistent iteration speed
+- Zero NaN batches — completely stable training
+- 29% perplexity improvement from Epoch 1 to 2
+- Training time: ~9h 12m on single T4 GPU
 
 ## 💡 Technical Innovation
 
 ### Perplexity-Based Routing (Novel Contribution)
 
 Traditional transformers apply the same computational depth to all inputs. This model recognizes that:
-- **Simple inputs** (greetings, common phrases) need minimal processing
-- **Complex inputs** (reasoning, technical content) benefit from deeper iterative refinement
+- **Simple inputs** need minimal processing
+- **Complex inputs** benefit from deeper iterative refinement
 
-**Key Innovation:** Instead of manual labeling, the model learns complexity from its own performance:
+**Key Innovation:** The model learns complexity from its own performance:
 ```python
-# During training:
+# During training (self-supervised):
 sample_perplexity = exp(sample_loss)
 
 if sample_perplexity < 20:
-    label = 0  # Simple - model is confident
+    label = 0  # Simple — model is confident
 elif sample_perplexity < 50:
-    label = 1  # Medium - model is uncertain  
+    label = 1  # Medium — model is uncertain
 else:
-    label = 2  # Complex - model struggling
+    label = 2  # Complex — model struggling
 
 # Router learns to predict this from input features
 router_loss = CrossEntropyLoss(router_logits, label)
+total_loss = lm_loss + 0.1 * router_loss
 ```
 
 **Benefits:**
-- ✅ **No manual labeling required** - fully self-supervised
-- ✅ **Adapts as model learns** - curriculum learning
-- ✅ **Objective measure** - based on actual model performance
-- ✅ **Efficient computation** - allocates resources intelligently
+- ✅ **No manual labeling** — fully self-supervised
+- ✅ **Adapts as model learns** — natural curriculum
+- ✅ **Objective measure** — based on actual model performance
+- ✅ **Efficient** — simple inputs use 1/5 the compute of complex ones
 
-### Rotary Positional Embeddings (RoPE)
+### NaN-Safe Attention Masking
 
-Uses RoPE instead of learned positional embeddings:
-- Better extrapolation to longer sequences
-- Relative position awareness
-- Improved performance on positional tasks
-- No learned position parameters
+Critical fix for stable fp16 training:
+```python
+# Unstable (causes NaN in fp16):
+mask.fill_(float('-inf'))
+
+# Stable ✅:
+mask.fill_(-1e4)
+# Then clamp before softmax:
+scores = scores.clamp(min=-1e4, max=1e4)
+attn = F.softmax(scores, dim=-1)
+attn = torch.nan_to_num(attn, nan=0.0)
+```
 
 ### Self-Supervised Curriculum Learning
 
-The model implements automatic curriculum learning:
 1. **Early training**: Most samples are "complex" (high perplexity)
 2. **Mid training**: Distribution shifts as model learns
 3. **Late training**: More samples become "simple" (low perplexity)
 
-This creates a natural curriculum without human intervention.
+Natural curriculum without human intervention.
 
 ## 🎯 Use Cases
 
 ### ✅ Recommended
-
-- **Educational demos** - Teaching language model concepts
-- **Research** - Experimenting with adaptive computation
-- **Prototyping** - Testing conversational AI applications
-- **Learning** - Understanding transformer architectures
-- **Creative writing assistance** - With human review
-- **Code explanation** - Basic programming concepts
+- **Research** — Experimenting with adaptive computation
+- **Educational demos** — Teaching language model concepts
+- **Prototyping** — Conversational AI applications
+- **Creative writing** — With human review
 
 ### ⚠️ Not Recommended
-
-- Production chatbots without human oversight
+- Production chatbots without fine-tuning
 - Medical, legal, or financial advice
-- Generating authoritative content without verification
-- Automated content moderation
 - Safety-critical systems
-- High-stakes decision making
 
 ## ⚠️ Limitations
 
-### Technical Limitations
-
-1. **Context Window**: 512 tokens maximum (vs 2048+ for modern models)
-2. **Training Data**: 37K samples - relatively small dataset
+1. **Context Window**: 512 tokens maximum
+2. **Training Data**: 150K samples — smaller than commercial models
 3. **Single Language**: Primarily English
-4. **Generation Quality**: Good but not perfect - may require post-processing
-5. **Knowledge Cutoff**: Training data up to early 2024
-
-### Known Issues
-
-- **Repetition**: May repeat phrases in very long generations (>200 tokens)
-- **Factual Accuracy**: Small knowledge base, may generate plausible but incorrect information
-- **Context Retention**: May lose track of earlier context in long conversations
-- **Domain Specificity**: Limited knowledge in highly specialized fields
-
-### Generation Characteristics
-
-- Occasionally incomplete sentences at sequence boundaries
-- May generate generic filler phrases
-- Temperature tuning recommended for optimal quality
-- Best results with clear, specific prompts
-
-## 🔬 Ethical Considerations
-
-### Bias & Fairness
-
-This model may exhibit biases from training data:
-
-**Potential Biases:**
-- Geographic: Western/English content overrepresentation
-- Demographic: Biases from web text and conversational data
-- Temporal: Reflects content patterns up to 2024
-- Topic: Conversational data may skew certain perspectives
-
-**Mitigation:**
-- Diverse training sources (Anthropic HH, UltraChat)
-- Quality filtering applied
-- Users should validate outputs for fairness-critical applications
-
-### Responsible Use
-
-**Environmental Impact:**
-- Training: ~84 minutes on T4 GPU
-- Estimated CO₂: ~0.09 kg (single training run)
-- Relatively low impact due to efficient training
-
-## 📖 Model Card
-
-### Model Details
-
-- **Developed by:** Girinath11
-- **Model type:** Causal Language Model with Adaptive Recursion
-- **Language:** English
-- **License:** MIT
-- **Base Model:** GPT-2 tokenizer
-- **Training Date:** March 2026
-- **Framework:** PyTorch + Transformers
-
-### Intended Use
-
-**Primary Use Cases:**
-- Research on adaptive computation
-- Educational demonstrations
-- Conversational AI prototyping
-- Understanding mixture-of-experts concepts
-- Curriculum learning research
-
-**Out-of-Scope:**
-- Production deployment without fine-tuning
-- High-stakes decision making
-- Content requiring domain expertise
-- Real-time applications requiring <100ms latency
-
-### Evaluation Methodology
-
-**Metrics:**
-- Primary: Perplexity on held-out validation set
-- Secondary: Training loss, generation quality
-
-**Evaluation Data:**
-- 2,000 samples from Anthropic HH & UltraChat
-- Same preprocessing as training
-- No overlap with training data
+4. **Repetition**: May repeat phrases in very long generations (>200 tokens)
+5. **Factual Accuracy**: May generate plausible but incorrect information
 
 ## 📁 Repository Structure
 ```
-recursive-language-model-90m/
-├── config.json                    # Model configuration
-├── model.safetensors             # Model weights (363 MB)
-├── tokenizer.json                # Tokenizer vocabulary
-├── tokenizer_config.json         # Tokenizer settings
-├── special_tokens_map.json       # Special token mappings
-├── model_info.json               # Training metadata
-├── mixture_of_recursion.py       # Model architecture code
-└── README.md                     # This file
+recursive-language-model-198m/
+├── config.json                 # Model configuration
+├── model.safetensors           # Model weights (794 MB)
+├── tokenizer.json              # Tokenizer vocabulary
+├── tokenizer_config.json       # Tokenizer settings
+├── special_tokens_map.json     # Special token mappings
+├��─ vocab.json                  # GPT-2 vocabulary
+├── merges.txt                  # BPE merges
+├── mixture_of_recursion.py     # Model architecture code
+├── model_usage.py              # Usage examples
+└── README.md                   # This file
 ```
 
-## 🔗 Links
-
-- **Model Card:** [Hugging Face](https://huggingface.co/Girinath11/recursive-language-model-90m)
-- **GitHub:** [Repository](https://github.com/Giri530/recursive-language-model-90m)
-
 ## 📄 Citation
 
-If you use this model in your research, please cite:
 ```bibtex
-@misc{recursive-lm-90m-2026,
+@misc{mixture-of-recursion-198m-2026,
   author = {Girinath11},
-  title = {Recursive Language Model with Perplexity-Based Dynamic Routing},
+  title = {Mixture of Recursion Language Model with Perplexity-Based Dynamic Routing},
   year = {2026},
   publisher = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/Girinath11/recursive-language-model-90m}},
-  note = {90M parameter model with adaptive computation achieving 17.45 perplexity}
+  howpublished = {\url{https://huggingface.co/Girinath11/recursive-language-model-198m}},
+  note = {198M parameter model with adaptive computation achieving 15.37 perplexity}
 }
 ```
 
@@ -513,31 +383,25 @@ If you use this model in your research, please cite:
 
 - **Anthropic** for HH-RLHF dataset
 - **Tsinghua University** for UltraChat dataset
+- **Vicgalle** for Alpaca-GPT4 dataset
 - **Hugging Face** for Transformers library
 - **Kaggle** for free GPU access
-- **OpenAI** for GPT-2 tokenizer
 
 ## 📝 Version History
 
 ### v1.0 (March 2026)
 - Initial release
-- 90.7M parameters
-- Perplexity: 17.45
-- Trained on 37K samples
-- 3 epochs, 84 minutes training time
-
-## 📧 Contact
-
-For questions, issues, or collaboration:
-- **GitHub Issues:** [Report a bug](https://github.com/Giri530/recursive-language-model-90m/issues)
-- **Hugging Face Discussions:** [Ask a question](https://huggingface.co/Girinath11/recursive-language-model-90m/discussions)
+- 198M parameters
+- Perplexity: 15.37
+- Trained on 150K samples
+- 2 epochs, 9h 12m training time
+- NaN-safe fp16 training
 
 ## 📜 License
 
-MIT License - Free to use with attribution
+MIT License — Free to use with attribution
 
 ---
 
-**Model Status:** ✅ Production Ready for Research & Prototyping
-
-**Last Updated:** March 2, 2026
+**Model Status:** ✅ Ready for Research & Prototyping  
+**Last Updated:** March 2026