Update Space README with Phase 2 status (Sept 21, 2025)

README.md

@@ -1,36 +1,146 @@
 ---
-title: B2NL Tokenizer v6.1.1 Demo
+title: Intelligent Tokenizer V6 Demo
 emoji: 🌍
 colorFrom: blue
 colorTo: green
 sdk: gradio
-sdk_version: 5.46.1
+sdk_version: 4.0.0
 app_file: app.py
 pinned: true
+license: apache-2.0
 models:
 - ggunio/B2NL-v6.1.1
-license: apache-2.0
 ---
 
-# B2NL v6.1.1: Byte-to-Natural-Language Tokenizer
+# 🌍 B2NL v6.1.1: Byte-to-Natural-Language Tokenizer Demo
+
+## 📢 Status Update (2025-09-21)
+
+### ✅ Phase 1: COMPLETE - 97.71% Reconstruction Achieved!
+### 🔄 Phase 2: IN PROGRESS - Dynamic Compression Training
+### 📅 Next Update: September 28, 2025 (Phase 2 Results)
+
+---
+
+## 🎯 Current Model Status
+
+This demo shows **B2NL (ByToNL) v6.1.1**, a revolutionary byte-level tokenizer that achieved:
+- **97.71% overall reconstruction rate**
+- **100% byte-exact reconstruction** for all 6 test languages
+- **No vocabulary files** - pure byte-level learning
+
+### ⚠️ Important Notes:
+1. **Current Scope**: 6 languages (NOT 204 yet)
+2. **Phase 2 Training**: Dynamic compression (1-50:1) in progress
+3. **204 Languages**: Will begin AFTER successful validation
+
+---
+
+## 📊 Phase 1 Results (COMPLETE)
+
+| Language | Byte-Exact | Character-Level | Edit Similarity | Status |
+|----------|------------|-----------------|-----------------|--------|
+| English  | 100.00%    | 100.00%         | 98.88%          | ✅ Perfect |
+| Korean   | 100.00%    | 100.00%         | 97.30%          | ✅ Perfect |
+| Japanese | 100.00%    | 100.00%         | 96.55%          | ✅ Perfect |
+| Chinese  | 100.00%    | 100.00%         | 96.30%          | ✅ Perfect |
+| Arabic   | 100.00%    | 100.00%         | 98.36%          | ✅ Perfect |
+| Spanish  | 100.00%    | 100.00%         | 98.88%          | ✅ Perfect |
+
+---
+
+## 🔄 Phase 2: Compression Training (IN PROGRESS)
+
+Currently training with dynamic compression ratios:
+- **High accuracy (>95%)**: Apply 30-50:1 compression
+- **Medium accuracy (90-95%)**: Apply 10-30:1 compression
+- **Low accuracy (<90%)**: Apply 1-10:1 compression
+
+**Target**: 3:1 average compression while maintaining >95% reconstruction
+
+---
+
+## 🚀 How to Use This Demo
+
+1. **Enter any text** in the input box
+2. **Choose generation mode**:
+   - Teacher Forcing: Better quality (uses ground truth)
+   - Autoregressive: Realistic inference
+3. **Click "Tokenize & Reconstruct"**
+4. See the reconstruction quality and compression ratio!
+
+---
+
+## 🎯 Key Features
+
+### Zero Vocabulary
+- No vocabulary files needed
+- Works with ANY text (any language, emoji, code)
+- Direct byte-level processing
+
+### Universal Coverage
+- Tested on 6 diverse languages
+- Plans for 204 languages (pending validation)
+- Handles mixed languages seamlessly
+
+### Efficient Architecture
+- 301.7M parameters (lightweight)
+- 5-layer encoder + 8-layer decoder
+- Fast inference on CPU/GPU
+
+---
+
+## 📅 Timeline
+
+### This Week (Sept 21-28, 2025)
+- Phase 2 compression training
+- Dynamic ratio testing
+- Performance monitoring
+
+### Next Week (Sept 28 - Oct 5, 2025)
+- **Phase 2 Results Release**
+- Compression achievements
+- Decision on 204-language expansion
+
+### Future (With GPU Support)
+- 204-language training
+- 2 weeks on A100 needed
+- Full FLORES-200 dataset
+
+---
+
+## 💡 Try These Examples
+
+The demo includes examples in:
+- 🇬🇧 English
+- 🇰🇷 Korean
+- 🇯🇵 Japanese
+- 🇨🇳 Chinese
+- 🇸🇦 Arabic
+- 🇪🇸 Spanish
+- 🇫🇷 French
+- 🇷🇺 Russian
+- 🚀 Even emojis!
 
-## 🎉 97.71% Reconstruction Achieved!
+---
 
-This space demonstrates our breakthrough byte-level tokenizer that achieves **100% byte-exact reconstruction** for all tested languages without any vocabulary files.
+## 📊 Technical Details
 
-### Key Features
-- **No Vocabulary**: Pure byte-level learning
-- **97.71% Overall Accuracy**: Near-perfect reconstruction
-- **6 Languages**: 100% byte-exact for each
-- **301.7M Parameters**: Efficient size
-- **Pure Learning**: No linguistic rules
+- **Parameters**: 301,739,670 (301.7M)
+- **Encoder**: 5 layers (768→896→1024→1152→1280)
+- **Decoder**: 8 layers (1280d)
+- **Vocab Size**: 260 (256 bytes + 4 special tokens)
 
-### Phase 1 Complete
-We've successfully completed Phase 1 training with outstanding results. Phase 2 (compression) starting soon!
+---
+
+## 🤝 Support & Links
+
+- **Model**: [ggunio/B2NL-v6.1.1](https://huggingface.co/ggunio/B2NL-v6.1.1)
+- **GitHub**: [Repository](https://github.com/Woojiggun/intelligent-tokenizer)
+- **Paper**: Coming soon after Phase 3
+
+---
 
-### Links
-- [Model](https://huggingface.co/ggunio/B2NL-v6.1.1)
-- [GitHub](https://github.com/Woojiggun/intelligent-tokenizer)
+**Note: This is a research project. Current model is 6 languages only. 204-language expansion pending validation and GPU resources.**
 
-### Support Us
-We need GPU resources to train on 204 languages. If you can help, please reach out!
+🚀 Watch this space for Phase 2 results next week!