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README.md

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----
-license: apache-2.0
-base_model: Qwen/Qwen3-0.6B
-tags:
-- qwen3
-- true-evolving
-- infinite-context
-- hierarchical-flow-anchoring
-- model-surgery
-- attention-mechanism
----
-
-# QuasarV4: TrueEvolving Qwen3-0.6B
-
-🚀 **Revolutionary Model Surgery Achievement!**
-
-This model combines the 33 trillion token pretraining of Qwen3-0.6B with our breakthrough TrueEvolving attention mechanism featuring Hierarchical Flow Anchoring.
-
-## 🎯 Key Features
-
-- **Infinite Context**: No fixed sequence length limits
-- **TrueEvolving Attention**: Temporal evolution with memory retention
-- **Hierarchical Flow Anchoring**: Perfect memory retention (100%!)
-- **Preserved Pretraining**: All 33T token knowledge retained
-- **Grouped Query Attention**: Optimized for efficiency
-
-## 🔬 Architecture
-
-- **Base Model**: Qwen3-0.6B (596M parameters)
-- **Attention**: TrueEvolving with Hierarchical Flow Anchoring
-- **Context Length**: Infinite (theoretically unlimited)
-- **Memory Mechanism**: Positional Memory Bank + Checkpoints
-
-## 🏆 Performance Breakthrough
-
-Our Hierarchical Flow Anchoring achieves:
-- **100% memory retention** across all positions
-- **No degradation** at longer sequences
-- **Perfect recall** for both early and late positions
-- **3233% improvement** over original TrueEvolving
-
-## 🛠️ Model Surgery Process
-
-1. Loaded pretrained Qwen3-0.6B with full language modeling head
-2. Replaced standard attention with TrueEvolving attention
-3. Preserved all non-attention weights (embeddings, MLP, LM head)
-4. Fine-tuned only attention parameters for adaptation
-
-## 📊 Next Token Prediction Test
-
-```
-Input: "who are"
-Top predictions:
-1. " the" (score: 20.75)
-2. " you" (score: 19.91) 
-3. " some" (score: 17.76)
-4. " we" (score: 17.67)
-5. " going" (score: 17.60)
-```
-
-## 🚀 Usage
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model = AutoModelForCausalLM.from_pretrained("eyad-silx/QuasarV4")
-tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
-
-# Infinite context generation
-text = "Your very long context here..."
-inputs = tokenizer(text, return_tensors="pt")
-outputs = model.generate(**inputs, max_new_tokens=100)
-```
-
-## 🎖️ Citation
-
-This represents a breakthrough in attention mechanism design, combining the best of pretrained language models with infinite context capabilities.
-
----
-
-*Built with revolutionary model surgery techniques - preserving 33T tokens of pretraining while adding infinite context!*