Update README.md

README.md

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 license: apache-2.0
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-OpenMOSE/HRWKV7-Reka-Flash3-Preview
+# HRWKV7-Reka-Flash3-Preview
 
+### Model Description
 
-This is experimental model.
+HRWKV7-Reka-Flash3-Preview is an experimental hybrid architecture model that combines RWKV v7's linear attention mechanism with Group Query Attention (GQA) layers. Built upon the Reka-flash3 21B foundation, this model replaces most Transformer attention blocks with RWKV blocks while strategically maintaining some GQA layers to enhance performance on specific tasks.
 
-BaseModel: Reka-Flash3 21B
+- **Developed by:** OpenMOSE
+- **Model type:** Hybrid Linear-Attention Language Model
+- **Language(s):** Multilingual (inherited from Reka-flash3 21B)
+- **License:** Apache-2.0
+- **Base Model:** Reka-flash3 21B
+- **Year:** 2025
 
-Architecture: Modified RWKV hxa079 (x070 based) + No Position Embedding GQA
+### Architecture Specifications
 
-Training ctx:4096
+- **Architecture:** RWKV v7 based "hxa079" Architecture + Group Query Attention Hybrid
+- **Total Layers:** 44 layers (L44D6114)
+  - 38 RWKV layers (with Rope)
+  - 6 GQA layers (No Rope, No Position Embeddings)
+- **Hidden Dimension:** 6144
+- **Training Context Window:** 4096 tokens
+- **Inference Context Window** 32768
 
-Suitable ctx: 32768
+## Technical Innovation
 
+### RWKV "hxa079" Architecture
+
+The model implements several key improvements over standard RWKV architectures:
+
+1. **Token Shift Removal**: Unlike traditional RWKV, the hxa079 variant removes token shifting mechanisms
+2. **GroupNorm Removal**: Eliminates GroupNorm layers for training stability
+3. **k_first Introduction**: Implements a novel k_first mechanism optimized for attention conversion
+
+### Hybrid Design Benefits
+
+- **Linear Attention Inference**: RWKV blocks enable O(1) memory complexity during inference
+- **Enhanced Needle Tasks**: Strategic placement of GQA layers significantly improves performance on needle-in-haystack retrieval tasks, addressing a known limitation of pure linear attention models
+- **Implicit Position Encoding**: Interestingly, the model achieves better performance when RoPE (Rotary Position Embedding) is not applied to GQA layers, suggesting that RWKV blocks provide implicit positional encoding capabilities
+
+## Intended Use
+
+This is an **experimental research model** designed to explore hybrid architectures combining linear and quadratic attention mechanisms. It is intended for:
+
+- Research into efficient attention mechanisms
+- Benchmarking hybrid architecture performance
+- Exploring linear attention limitations and solutions
+- Academic and industrial R&D purposes
+
+## Limitations
+
+- **Experimental Status**: This model is in experimental stages and may exhibit unexpected behaviors
+- **Context Window**: Limited to 4096 tokens during training, though RWKV architecture theoretically supports longer sequences
+- **Performance Variability**: As a hybrid model, performance may vary significantly across different task types
+
+## Training Details
+
+- **Training Context Window:** 4096 tokens
+- **Base Model Initialization:** Weights initialized from Reka-flash3 21B
+- **Architecture Conversion:** Transformer attention blocks systematically replaced with RWKV blocks, except for 6 strategically placed GQA layers
+
+## Evaluation
+
+Performance evaluation is ongoing. The model shows promising results in:
+- Maintaining base model capabilities while achieving linear attention efficiency
+- Significantly improved needle-in-haystack task performance compared to pure RWKV architectures
+- Competitive performance on standard language modeling benchmarks
+
+## Thank you for Big help :)
+- SmerkyG Inspired by RADLADS (https://arxiv.org/abs/2505.03005) 
+
+
+## Model Card Contact
+
+OpenMOSE - 2025
+
+---
+
+*Note: This is an experimental model. Performance characteristics and behaviors may differ from both pure RWKV and standard Transformer architectures. Users should thoroughly evaluate the model for their specific use cases.*