Upload Glaurung Large 001 - RoBERTa large model for binary analysis

README.md

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 # Glaurung Large 001
 
-A RoBERTa-based masked language model trained on binary executable files for security research and binary analysis.
+A RoBERTa-based masked language model trained on binary executable files for security research and binary analysis. Part of the [Glaurung](https://github.com/mjbommar/glaurung) project: a modern reverse engineering framework with first-class AI integration.
 
 ## Overview
 
 **Glaurung Large 001** is a transformer model specifically designed for understanding binary executable files. It uses a custom BPE (Byte Pair Encoding) tokenizer trained on multi-byte patterns from various binary formats across multiple architectures (x86-64, ARM64, etc.) and operating systems (Linux, Alpine, Ubuntu, Debian, Rocky).
 
+This is the **large variant** (371M parameters, 24 layers) offering enhanced understanding of binary patterns. For faster inference, see [glaurung-small-001](https://huggingface.co/mjbommar/glaurung-small-001) (160M parameters).
+
 ### Key Features
 - **Custom Binary Tokenizer**: BPE tokenizer that creates efficient multi-byte tokens from binary data
 - **Binary-Aware**: Trained on actual executable files, not hex strings
@@ -38,6 +40,7 @@ A RoBERTa-based masked language model trained on binary executable files for sec
 - **Attention Heads**: 16
 - **Intermediate Size**: 4096
 - **Vocabulary Size**: 65,536 tokens
+- **Tokenizer**: [binary-tokenizer-005](https://huggingface.co/mjbommar/binary-tokenizer-005)
 - **Max Position Embeddings**: 520
 - **Parameters**: ~371M
 - **Special Tokens**:
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   - `<|mask|>` (5): Mask token for MLM
   - `<|unk|>` (6): Unknown token
 
+## Glaurung Ecosystem
+
+This model is part of the **Glaurung** project ecosystem:
+
+### 🔧 Main Project
+- **[Glaurung](https://github.com/mjbommar/glaurung)** - A modern reverse engineering framework designed to replace Ghidra with first-class AI integration throughout the analysis pipeline. Built with Rust's performance and Python's accessibility, featuring AI agents integrated at every level from format detection to decompilation.
+
+### 🤖 Model Family
+- **[glaurung-large-001](https://huggingface.co/mjbommar/glaurung-large-001)** (this model) - 371M parameters, 24 layers
+- **[glaurung-small-001](https://huggingface.co/mjbommar/glaurung-small-001)** - 160M parameters, 12 layers, faster inference
+
+### 🔤 Tokenizer
+- **[binary-tokenizer-005](https://huggingface.co/mjbommar/binary-tokenizer-005)** - 65K vocabulary BPE tokenizer trained on multi-byte patterns
+
 ## Performance Comparison vs Glaurung Small 001
 
 | Metric | Glaurung Small 001 | Glaurung Large 001 | Improvement |