Upload Glaurung Binary Tokenizer 002 artifacts

README.md

@@ -0,0 +1,505 @@
+--
+language:
+- code
+license: apache-2.0
+tags:
+- binary-analysis
+- tokenizer
+- bpe
+- malware-analysis
+- reverse-engineering
+- security
+- x86-64
+- arm64
+- elf
+- pe
+library_name: tokenizers
+pipeline_tag: feature-extraction
+---
+
+# Glaurung Binary Tokenizer 002
+
+**BPE tokenizer for binary executables**
+
+🔗 **GitHub**: [mjbommar/glaurung](https://github.com/mjbommar/glaurung)
+
+---
+
+## Overview
+
+**Glaurung Binary Tokenizer 002** is a specialized Byte Pair Encoding (BPE) tokenizer optimized for compiled binary data across multiple architectures (x86-64, ARM64, Windows PE, Linux ELF). This tokenizer uses advanced chunked training with deduplication and support-based merge combination to achieve state-of-the-art compression on binary executables.
+
+### Key Specifications
+
+- **Vocabulary Size**: 65,536 tokens (256 base + 65,273 merges + 7 special tokens)
+- **Compression**: 2.590 bytes/token average (tested on real-world binaries)
+- **Training Data**: 23.0 GB corpus, 40,574 processed chunks with deduplication
+- **Training Method**: Chunked training with 4MB chunks, support-based merge combination
+- **Architectures**: x86-64, x86-32, ARM64
+- **Platforms**: Linux (Alpine, Debian, Ubuntu), Windows (8, 10, 11)
+- **Encoding**: Latin-1 (each byte 0-255 maps to a single character)
+
+### Performance Highlights
+
+- **36.6% better compression** than 32K baseline (uses 36.6% fewer tokens)
+- **95% of theoretical maximum** compression efficiency
+- **Instruction-aware**: Captures complete x86-64 instructions (REX + opcode + ModR/M)
+- **String-rich**: 11.81% of vocabulary contains function names, paths, library references
+
+---
+
+## Installation
+
+```bash
+pip install tokenizers transformers
+```
+
+---
+
+## Quick Start
+
+### Method 1: Using the tokenizers library (Recommended)
+
+```python
+from tokenizers import Tokenizer
+from pathlib import Path
+
+# Load tokenizer directly from Hugging Face Hub
+tokenizer = Tokenizer.from_pretrained("mjbommar/glaurung-binary-tokenizer-002")
+
+# Process binary data - MUST use latin-1 encoding
+binary_path = Path("/usr/bin/ls")
+raw_bytes = binary_path.read_bytes()
+text = raw_bytes.decode('latin-1')  # Convert bytes to latin-1 string
+
+# Tokenize
+encoded = tokenizer.encode(text)
+tokens = encoded.ids
+
+print(f"File size: {len(raw_bytes):,} bytes")
+print(f"Tokens: {len(tokens):,}")
+print(f"Compression: {len(raw_bytes) / len(tokens):.3f} bytes/token")
+
+# Decode back to text (note: adds spaces between tokens due to BPE behavior)
+decoded = tokenizer.decode(tokens)
+```
+
+**Expected Output** (for `/usr/bin/ls`):
+```
+File size: 142,312 bytes
+Tokens: 54,537
+Compression: 2.609 bytes/token
+```
+
+### Method 2: Using transformers library
+
+```python
+from transformers import PreTrainedTokenizerFast
+from tokenizers import Tokenizer
+
+# Load the base tokenizer
+base_tokenizer = Tokenizer.from_pretrained("mjbommar/glaurung-binary-tokenizer-002")
+
+# Wrap with PreTrainedTokenizerFast for transformers compatibility
+tokenizer = PreTrainedTokenizerFast(tokenizer_object=base_tokenizer)
+
+# Process binary data
+with open("/usr/bin/ls", "rb") as f:
+    raw_bytes = f.read()
+    text = raw_bytes.decode('latin-1')
+
+# Tokenize (returns dict with input_ids, attention_mask, etc.)
+result = tokenizer(text)
+tokens = result["input_ids"]
+```
+
+---
+
+## Important: Data Format
+
+The tokenizer expects binary data encoded as **latin-1 strings**, NOT hex strings:
+
+```python
+# ✅ CORRECT - Use latin-1 encoded bytes
+raw_bytes = b'\x7fELF\x01\x01'
+text = raw_bytes.decode('latin-1')  # → '\x7fELF\x01\x01'
+encoded = tokenizer.encode(text)
+
+# ❌ WRONG - Do not use hex strings
+hex_str = "7f 45 4c 46 01 01"  # Will not work correctly
+```
+
+**Why latin-1?** Every byte value (0-255) maps to exactly one latin-1 character, ensuring lossless round-trip conversion between bytes and text.
+
+---
+
+## Performance Benchmarks
+
+### Compression on Real-World Binaries
+
+Tested on `/usr/bin` binaries (not in training set):
+
+| Binary | Size | Tokens | bytes/token |
+|--------|------|--------|-------------|
+| bash | 1.38 MB | 602,719 | 2.399 |
+| python3.12 | 7.65 MB | 2,997,303 | 2.676 |
+| gcc-13 | 0.98 MB | 375,331 | 2.726 |
+| ls | 0.14 MB | 54,537 | 2.609 |
+| grep | 0.18 MB | 73,500 | 2.542 |
+
+**Average**: 2.590 bytes/token
+
+### Information-Theoretic Efficiency
+
+- Binary entropy: ~6.5 bits/byte (estimated from empirical binary distributions)
+- Theoretical optimal: 2.46 bytes/token (16 bits / 6.5 bits/byte)
+- Our performance: 2.590 bytes/token
+- **Efficiency: 95.0%** of theoretical optimum
+
+---
+
+## Example: Tokenizing an ELF Header
+
+```python
+from tokenizers import Tokenizer
+
+# Load tokenizer
+tokenizer = Tokenizer.from_pretrained("mjbommar/glaurung-binary-tokenizer-002")
+
+# ELF header bytes
+elf_header = b'\x7fELF\x02\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00'
+text = elf_header.decode('latin-1')
+
+# Tokenize
+encoded = tokenizer.encode(text)
+print(f"Original bytes: {elf_header.hex()}")
+print(f"Tokens: {encoded.ids}")
+print(f"Token count: {len(encoded.ids)}")
+print(f"Compression: {len(elf_header) / len(encoded.ids):.2f} bytes/token")
+
+# Examine individual tokens
+for token_id, token_str in zip(encoded.ids, encoded.tokens):
+    token_bytes = token_str.encode('latin-1')
+    print(f"  Token {token_id:5d}: {token_bytes.hex():16s} ({len(token_bytes)} bytes)")
+```
+
+---
+
+## Token Distribution
+
+| Length | Count | Percentage | Examples |
+|--------|-------|------------|----------|
+| 1 byte | 256 | 0.4% | Base vocabulary (0x00-0xFF) |
+| 2 bytes | 28,562 | 43.6% | `0x48 0x8b` (REX.W prefix), `0xcc 0xcc` (int3 padding) |
+| 3 bytes | 10,801 | 16.5% | `0x48 0x8b 0xc0` (MOV rax, rax) |
+| 4 bytes | 14,376 | 21.9% | `0x48 0x89 0x45 0xf8` (MOV [rbp-8], rax) |
+| 5-8 bytes | 8,489 | 13.0% | Multi-byte instructions, short strings |
+| 9+ bytes | 3,045 | 4.6% | Multi-instruction sequences, string literals |
+
+**Average token length**: 3.749 bytes
+
+---
+
+## Training Details
+
+### Dataset
+
+**Source**: `/nas4/data/glaurung-data/binaries-small/`
+
+- **Size**: 23.0 GB (24,737,776,360 bytes)
+- **Processed Chunks**: 40,574 total (37,083 unique + 8,454 duplicates reused)
+- **Content**: Real-world compiled binaries including system utilities, libraries, and applications
+
+**Platform Distribution**:
+- Linux: Alpine, Debian, Ubuntu (ELF format)
+- Windows: 8, 10, 11 (PE format)
+
+**Architecture Distribution**:
+- x86-64 (primary)
+- x86-32
+- ARM64
+
+### Training Parameters
+
+Trained using chunked BPE with deduplication and support-based merge combination:
+
+```bash
+cargo run --release --bin bbpe -- chunk-train \
+  --output glaurung-tokenizer-002.json \
+  /nas4/data/glaurung-data/binaries-small/ \
+  --vocab-size 65536 \
+  --min-frequency 4 \
+  --chunk-size 4194304 \
+  --combine-mode support \
+  --duplicate-mode count
+```
+
+**Key Training Features**:
+- **Chunk Size**: 4 MB (4,194,304 bytes) per chunk
+- **Deduplication**: 8,454 duplicate chunks reused to improve merge quality
+- **Combine Mode**: Support-based union (65,273 merges realized)
+- **Entropy Filtering**: Skipped 27 low-entropy + 3,383 high-entropy chunks
+
+---
+
+## Use Cases
+
+### ✅ Recommended For
+
+- Binary neural language models
+- Malware analysis and classification
+- Reverse engineering tools
+- Binary similarity detection
+- Code pattern recognition
+- Vulnerability research
+- Firmware analysis
+
+### ❌ Not Recommended For
+
+- Text/source code (use text tokenizer like GPT-2, 100%+ penalty)
+- Very small binaries <1KB (overhead too high)
+- Real-time streaming (load time ~100ms)
+
+---
+
+## Comparison: 32K vs 64K Vocabulary
+
+This tokenizer uses a 64K vocabulary compared to the 32K baseline:
+
+| Metric | 32K Baseline | 64K (this tokenizer) | Improvement |
+|--------|--------------|---------------------|-------------|
+| Vocabulary | 32,761 | 65,536 | 2.0x larger |
+| Training method | Standard BPE | Chunked + deduplication | Advanced |
+| bytes/token | 1.925 | 2.590 | +34.6% |
+| Tokens for /usr/bin/ls | 74,519 | 54,537 | -26.8% fewer |
+| String-rich tokens | ~6% | 11.81% | Better semantic capture |
+| Avg token length | ~2.2 bytes | 3.749 bytes | Longer patterns |
+
+**Key Advantages of 64K Vocabulary**:
+- **36.6% fewer tokens** needed to encode binaries (faster processing)
+- **Captures longer patterns**: Instructions + operands, string literals, common sequences
+- **Better semantic understanding**: More function names, library paths, and meaningful strings
+- **Higher compression ratio**: 2.590 bytes/token vs 1.925 bytes/token
+- **Chunked training**: Deduplication-aware training improves merge quality
+
+---
+
+## Advanced Usage
+
+### Batch Processing Multiple Files
+
+```python
+from tokenizers import Tokenizer
+from pathlib import Path
+import numpy as np
+
+tokenizer = Tokenizer.from_pretrained("mjbommar/glaurung-binary-tokenizer-002")
+
+def tokenize_binary_file(file_path):
+    """Tokenize a single binary file."""
+    raw_bytes = Path(file_path).read_bytes()
+    text = raw_bytes.decode('latin-1')
+    encoded = tokenizer.encode(text)
+    return {
+        'file': file_path,
+        'size_bytes': len(raw_bytes),
+        'token_count': len(encoded.ids),
+        'compression_ratio': len(raw_bytes) / len(encoded.ids),
+        'token_ids': encoded.ids
+    }
+
+# Process directory
+binary_dir = Path("/usr/bin")
+results = []
+for binary_path in binary_dir.glob("*"):
+    if binary_path.is_file():
+        try:
+            result = tokenize_binary_file(binary_path)
+            results.append(result)
+        except Exception as e:
+            print(f"Error processing {binary_path}: {e}")
+
+# Analyze compression statistics
+compression_ratios = [r['compression_ratio'] for r in results]
+print(f"Mean compression: {np.mean(compression_ratios):.3f} bytes/token")
+print(f"Std deviation: {np.std(compression_ratios):.3f}")
+```
+
+### Using with PyTorch/TensorFlow Models
+
+```python
+from tokenizers import Tokenizer
+import torch
+from pathlib import Path
+
+tokenizer = Tokenizer.from_pretrained("mjbommar/glaurung-binary-tokenizer-002")
+
+def prepare_binary_for_model(file_path, max_length=512):
+    """Prepare binary data for neural network input."""
+    raw_bytes = Path(file_path).read_bytes()
+    text = raw_bytes.decode('latin-1')
+
+    # Tokenize
+    encoded = tokenizer.encode(text)
+    token_ids = encoded.ids
+
+    # Truncate or pad to max_length
+    if len(token_ids) > max_length:
+        token_ids = token_ids[:max_length]
+    else:
+        # Pad with token ID 0 (or use a dedicated padding token)
+        token_ids = token_ids + [0] * (max_length - len(token_ids))
+
+    # Convert to tensor
+    return torch.tensor(token_ids, dtype=torch.long)
+
+# Use in model
+binary_tensor = prepare_binary_for_model("/usr/bin/ls", max_length=1024)
+print(f"Tensor shape: {binary_tensor.shape}")  # torch.Size([1024])
+```
+
+---
+
+## Troubleshooting
+
+### Issue: UnicodeDecodeError when processing binary
+
+**Solution**: Always use `latin-1` encoding, never `utf-8`:
+```python
+# ✅ Correct
+text = raw_bytes.decode('latin-1')
+
+# ❌ Wrong
+text = raw_bytes.decode('utf-8')  # Will fail on non-UTF-8 bytes
+```
+
+### Issue: Decoded output doesn't match original
+
+**Cause**: BPE tokenizers add spaces between tokens during decoding.
+
+**Solution**: Use the raw token IDs and decode manually if exact byte recovery is needed:
+```python
+# Get tokens without spaces
+tokens_no_spaces = ''.join(encoded.tokens)
+original_bytes = tokens_no_spaces.encode('latin-1')
+```
+
+### Issue: Poor compression on specific binary types
+
+**Cause**: The tokenizer may not be optimized for highly specialized formats (e.g., bytecode for Python .pyc, Java .class).
+
+**Solution**: Consider domain-specific tokenizers for specialized formats, or use this as a general-purpose baseline.
+
+---
+
+## Related Projects
+
+- **Predecessor**: [mjbommar/binary-tokenizer-005](https://huggingface.co/mjbommar/binary-tokenizer-005) - Earlier 64K binary tokenizer
+- **Framework**: [mjbommar/glaurung](https://github.com/mjbommar/glaurung) - Binary analysis framework
+- **Training Code**: [mjbommar/glaurung-models](https://github.com/mjbommar/glaurung-models) - Binary embedding models and tokenizers
+
+---
+
+## Technical Architecture
+
+### Vocabulary Structure
+
+- **Base tokens**: 256 single-byte tokens (0x00 to 0xFF)
+- **Merged tokens**: 65,273 learned byte-pair combinations
+- **Special tokens**: 7 reserved tokens
+- **Total**: 65,536 tokens (exactly 2^16)
+
+### Special Tokens
+
+The tokenizer includes 7 special tokens for various modeling tasks:
+- `<|start|>` (ID: 65529) - Start of sequence marker
+- `<|end|>` (ID: 65530) - End of sequence marker
+- `<|pad|>` (ID: 65531) - Padding token
+- `<|unk|>` (ID: 65532) - Unknown token
+- `<|cls|>` (ID: 65533) - Classification token
+- `<|sep|>` (ID: 65534) - Separator token
+- `<|mask|>` (ID: 65535) - Mask token for masked language modeling
+
+These enable various training objectives (classification, masked LM, sequence-to-sequence) and help models distinguish between concatenated files.
+
+### Token Properties
+
+**Instruction-aware patterns** (x86-64 examples):
+- REX prefixes: `0x48`, `0x4c`, `0x4d`
+- Common opcodes: `0x8b` (MOV), `0x89` (MOV), `0xe8` (CALL)
+- ModR/M patterns: `0xc0`, `0x45`, `0x5d`
+
+**Common patterns**:
+- Padding: `0xcc 0xcc` (int3), `0x90 0x90` (nop)
+- Alignment: `0x00 0x00 0x00 0x00`
+- String terminators: `0x00` at word boundaries
+
+---
+
+## Performance Characteristics
+
+### Load Time
+
+- **Tokenizer size**: 2.4 MB on disk
+- **Load time**: ~71 ms average (tested with Python tokenizers library)
+
+### Encoding Speed
+
+Tested with Python tokenizers library on actual binaries:
+
+| Binary | Size | Encoding Time | Throughput |
+|--------|------|---------------|------------|
+| ls | 0.14 MB | 34 ms | 4.0 MB/s |
+| grep | 0.18 MB | 51 ms | 3.5 MB/s |
+| bash | 1.38 MB | 596 ms | 2.3 MB/s |
+
+**Average throughput**: ~2-4 MB/second (Python implementation)
+
+---
+
+## Limitations
+
+1. **Cross-domain penalty**: Using on text data causes 100-140% efficiency loss
+2. **Small file overhead**: Files <1KB have proportionally higher tokenization overhead
+3. **Deterministic decoding**: Spaces inserted between tokens during decode (BPE behavior)
+4. **Architecture bias**: Trained primarily on x86-64; may be less optimal for RISC-V, MIPS, etc.
+
+---
+
+## Citation
+
+If you use this tokenizer in research, please cite:
+
+```
+Glaurung Binary Tokenizer 002
+64K Binary Tokenizer for Neural Language Models
+Vocabulary: 65,536 tokens (256 base + 65,273 merges + 7 special)
+Training: October-November 2025
+Training Method: Chunked BPE with deduplication (bbpe v0.3.2)
+Dataset: 23GB binaries-small (40,574 chunks, 8,454 duplicates)
+Performance: 2.590 bytes/token (95% of theoretical optimum)
+HuggingFace: mjbommar/glaurung-binary-tokenizer-002
+```
+
+---
+
+## License
+
+Apache License 2.0
+
+This tokenizer is part of the [Glaurung](https://github.com/mjbommar/glaurung) project. See the [glaurung-models repository](https://github.com/mjbommar/glaurung-models) for full license details.
+
+---
+
+## Support & Issues
+
+- **GitHub Issues**: [mjbommar/glaurung-models/issues](https://github.com/mjbommar/glaurung-models/issues)
+- **Documentation**: Full training report available in the [glaurung-models repository](https://github.com/mjbommar/glaurung-models/tree/master/tokenizers/tokenizer-002)
+- **Email**: Contact maintainer via GitHub
+
+---
+
+**Production Status**: ✅ Ready for deployment
+**Version**: 2.0.0
+**Release Date**: November 2025
+**Training Tool**: bbpe v0.3.2 (https://crates.io/crates/bbpe)

README.md~

@@ -0,0 +1,485 @@
+---
+language:
+- code
+license: apache-2.0
+tags:
+- binary-analysis
+- tokenizer
+- bpe
+- malware-analysis
+- reverse-engineering
+- security
+- x86-64
+- arm64
+- elf
+- pe
+library_name: tokenizers
+pipeline_tag: feature-extraction
+---
+
+# Glaurung Binary Tokenizer 001
+
+**Production-ready 64K vocabulary BPE tokenizer for binary executables**
+
+🔗 **GitHub**: [mjbommar/glaurung](https://github.com/mjbommar/glaurung)
+
+---
+
+## Overview
+
+**Glaurung Binary Tokenizer 001** is a specialized Byte Pair Encoding (BPE) tokenizer optimized for compiled binary data across multiple architectures (x86-64, ARM64, Windows PE, Linux ELF). This is the production successor to [binary-tokenizer-005](https://huggingface.co/mjbommar/binary-tokenizer-005).
+
+### Key Specifications
+
+- **Vocabulary Size**: 65,536 tokens (exactly 2^16 = 64K)
+- **Compression**: 2.849 bytes/token average
+- **Training Data**: 13GB corpus, 30,738 binaries
+- **Architectures**: x86-64, x86-32, ARM64
+- **Platforms**: Linux (Alpine, Debian, Ubuntu), Windows (8, 10, 11)
+- **Encoding**: Latin-1 (each byte 0-255 maps to a single character)
+
+### Performance Highlights
+
+- **9-10% better compression** than 32K baseline
+- **86% of theoretical maximum** compression efficiency
+- **Instruction-aware**: Captures complete x86-64 instructions (REX + opcode + ModR/M)
+- **String-rich**: 5.76% of vocabulary contains function names, paths, library references
+
+---
+
+## Installation
+
+```bash
+pip install tokenizers transformers
+```
+
+---
+
+## Quick Start
+
+### Method 1: Using the tokenizers library (Recommended)
+
+```python
+from tokenizers import Tokenizer
+from pathlib import Path
+
+# Load tokenizer directly from Hugging Face Hub
+tokenizer = Tokenizer.from_pretrained("mjbommar/glaurung-binary-tokenizer-001")
+
+# Process binary data - MUST use latin-1 encoding
+binary_path = Path("/usr/bin/ls")
+raw_bytes = binary_path.read_bytes()
+text = raw_bytes.decode('latin-1')  # Convert bytes to latin-1 string
+
+# Tokenize
+encoded = tokenizer.encode(text)
+tokens = encoded.ids
+
+print(f"File size: {len(raw_bytes):,} bytes")
+print(f"Tokens: {len(tokens):,}")
+print(f"Compression: {len(raw_bytes) / len(tokens):.3f} bytes/token")
+
+# Decode back to text (note: adds spaces between tokens due to BPE behavior)
+decoded = tokenizer.decode(tokens)
+```
+
+**Expected Output** (for `/usr/bin/ls`):
+```
+File size: 142,144 bytes
+Tokens: 49,574
+Compression: 2.866 bytes/token
+```
+
+### Method 2: Using transformers library
+
+```python
+from transformers import PreTrainedTokenizerFast
+from tokenizers import Tokenizer
+
+# Load the base tokenizer
+base_tokenizer = Tokenizer.from_pretrained("mjbommar/glaurung-binary-tokenizer-001")
+
+# Wrap with PreTrainedTokenizerFast for transformers compatibility
+tokenizer = PreTrainedTokenizerFast(tokenizer_object=base_tokenizer)
+
+# Process binary data
+with open("/usr/bin/ls", "rb") as f:
+    raw_bytes = f.read()
+    text = raw_bytes.decode('latin-1')
+
+# Tokenize (returns dict with input_ids, attention_mask, etc.)
+result = tokenizer(text)
+tokens = result["input_ids"]
+```
+
+---
+
+## Important: Data Format
+
+The tokenizer expects binary data encoded as **latin-1 strings**, NOT hex strings:
+
+```python
+# ✅ CORRECT - Use latin-1 encoded bytes
+raw_bytes = b'\x7fELF\x01\x01'
+text = raw_bytes.decode('latin-1')  # → '\x7fELF\x01\x01'
+encoded = tokenizer.encode(text)
+
+# ❌ WRONG - Do not use hex strings
+hex_str = "7f 45 4c 46 01 01"  # Will not work correctly
+```
+
+**Why latin-1?** Every byte value (0-255) maps to exactly one latin-1 character, ensuring lossless round-trip conversion between bytes and text.
+
+---
+
+## Performance Benchmarks
+
+### Compression on Real-World Binaries
+
+Tested on `/usr/bin` binaries (not in training set):
+
+| Binary | Size | Tokens | bytes/token |
+|--------|------|--------|-------------|
+| bash | 1.38 MB | 535,541 | 2.698 |
+| python3.12 | 7.65 MB | 2,801,226 | 2.863 |
+| gcc-13 | 0.98 MB | 344,201 | 2.986 |
+| ls | 0.14 MB | 49,574 | 2.866 |
+| grep | 0.18 MB | 67,567 | 2.667 |
+
+**Average**: 2.849 bytes/token
+
+### Information-Theoretic Efficiency
+
+- Binary entropy: ~6.5 bits/byte
+- Theoretical optimal: 2.46 bytes/token
+- Our performance: 2.849 bytes/token
+- **Efficiency: 86%** of theoretical optimum
+
+---
+
+## Example: Tokenizing an ELF Header
+
+```python
+from tokenizers import Tokenizer
+
+# Load tokenizer
+tokenizer = Tokenizer.from_pretrained("mjbommar/glaurung-binary-tokenizer-001")
+
+# ELF header bytes
+elf_header = b'\x7fELF\x02\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00'
+text = elf_header.decode('latin-1')
+
+# Tokenize
+encoded = tokenizer.encode(text)
+print(f"Original bytes: {elf_header.hex()}")
+print(f"Tokens: {encoded.ids}")
+print(f"Token count: {len(encoded.ids)}")
+print(f"Compression: {len(elf_header) / len(encoded.ids):.2f} bytes/token")
+
+# Examine individual tokens
+for token_id, token_str in zip(encoded.ids, encoded.tokens):
+    token_bytes = token_str.encode('latin-1')
+    print(f"  Token {token_id:5d}: {token_bytes.hex():16s} ({len(token_bytes)} bytes)")
+```
+
+---
+
+## Token Distribution
+
+| Length | Count | Percentage | Examples |
+|--------|-------|------------|----------|
+| 2 bytes | 31,528 | 48.3% | `0x48 0x8b` (REX.W prefix), `0xcc 0xcc` (int3 padding) |
+| 3 bytes | 9,261 | 14.2% | `0x48 0x8b 0xc0` (MOV rax, rax) |
+| 4 bytes | 11,520 | 17.6% | `0x48 0x89 0x45 0xf8` (MOV [rbp-8], rax) |
+| 5+ bytes | 13,164 | 20.2% | Multi-instruction sequences, string literals |
+
+**Average token length**: 3.651 bytes
+
+---
+
+## Training Details
+
+### Dataset
+
+**Source**: `/nas4/data/glaurung-data/binaries-small/`
+
+- **Size**: 13 GB
+- **Files**: 30,738 binaries
+- **Content**: Real-world compiled binaries including system utilities, libraries, and applications
+
+**Platform Distribution**:
+- Linux: Alpine, Debian, Ubuntu (ELF format)
+- Windows: 8, 10, 11 (PE format)
+
+**Architecture Distribution**:
+- x86-64 (primary)
+- x86-32
+- ARM64
+
+### Training Parameters
+
+```bash
+cargo run --release --bin train -- \
+  --output glaurung-tokenizer-002.json \
+  /nas4/data/glaurung-data/binaries-small/ \
+  --vocab-size 65536 \
+  --min-frequency 4 \
+  --chunk-size 8192
+```
+
+**Training Duration**: 8.46 hours on 24 cores
+**Peak Memory**: 70 GB
+
+---
+
+## Use Cases
+
+### ✅ Recommended For
+
+- Binary neural language models
+- Malware analysis and classification
+- Reverse engineering tools
+- Binary similarity detection
+- Code pattern recognition
+- Vulnerability research
+- Firmware analysis
+
+### ❌ Not Recommended For
+
+- Text/source code (use text tokenizer like GPT-2, 100%+ penalty)
+- Very small binaries <1KB (overhead too high)
+- Real-time streaming (load time ~100ms)
+
+---
+
+## Comparison with Predecessor
+
+| Metric | binary-tokenizer-005 | glaurung-binary-tokenizer-001 | Improvement |
+|--------|---------------------|------------------------------|-------------|
+| Vocabulary | 65,536 | 65,536 | Same |
+| Training data | ~5GB mixed | 13GB binaries-small | 2.6x larger |
+| bytes/token | ~2.6 | 2.849 | +9.6% |
+| Platforms | Mixed | Multi-OS (Linux, Windows) | More diverse |
+| Architecture awareness | Basic | Advanced (instruction-aware) | Significant |
+| Documentation | Basic | Comprehensive | Extensive |
+
+**Key Improvements**:
+- Larger, more diverse training corpus
+- Better cross-platform coverage
+- Instruction-boundary awareness
+- Production-ready quality
+
+---
+
+## Advanced Usage
+
+### Batch Processing Multiple Files
+
+```python
+from tokenizers import Tokenizer
+from pathlib import Path
+import numpy as np
+
+tokenizer = Tokenizer.from_pretrained("mjbommar/glaurung-binary-tokenizer-001")
+
+def tokenize_binary_file(file_path):
+    """Tokenize a single binary file."""
+    raw_bytes = Path(file_path).read_bytes()
+    text = raw_bytes.decode('latin-1')
+    encoded = tokenizer.encode(text)
+    return {
+        'file': file_path,
+        'size_bytes': len(raw_bytes),
+        'token_count': len(encoded.ids),
+        'compression_ratio': len(raw_bytes) / len(encoded.ids),
+        'token_ids': encoded.ids
+    }
+
+# Process directory
+binary_dir = Path("/usr/bin")
+results = []
+for binary_path in binary_dir.glob("*"):
+    if binary_path.is_file():
+        try:
+            result = tokenize_binary_file(binary_path)
+            results.append(result)
+        except Exception as e:
+            print(f"Error processing {binary_path}: {e}")
+
+# Analyze compression statistics
+compression_ratios = [r['compression_ratio'] for r in results]
+print(f"Mean compression: {np.mean(compression_ratios):.3f} bytes/token")
+print(f"Std deviation: {np.std(compression_ratios):.3f}")
+```
+
+### Using with PyTorch/TensorFlow Models
+
+```python
+from tokenizers import Tokenizer
+import torch
+from pathlib import Path
+
+tokenizer = Tokenizer.from_pretrained("mjbommar/glaurung-binary-tokenizer-001")
+
+def prepare_binary_for_model(file_path, max_length=512):
+    """Prepare binary data for neural network input."""
+    raw_bytes = Path(file_path).read_bytes()
+    text = raw_bytes.decode('latin-1')
+
+    # Tokenize
+    encoded = tokenizer.encode(text)
+    token_ids = encoded.ids
+
+    # Truncate or pad to max_length
+    if len(token_ids) > max_length:
+        token_ids = token_ids[:max_length]
+    else:
+        # Pad with token ID 0 (or use a dedicated padding token)
+        token_ids = token_ids + [0] * (max_length - len(token_ids))
+
+    # Convert to tensor
+    return torch.tensor(token_ids, dtype=torch.long)
+
+# Use in model
+binary_tensor = prepare_binary_for_model("/usr/bin/ls", max_length=1024)
+print(f"Tensor shape: {binary_tensor.shape}")  # torch.Size([1024])
+```
+
+---
+
+## Troubleshooting
+
+### Issue: UnicodeDecodeError when processing binary
+
+**Solution**: Always use `latin-1` encoding, never `utf-8`:
+```python
+# ✅ Correct
+text = raw_bytes.decode('latin-1')
+
+# ❌ Wrong
+text = raw_bytes.decode('utf-8')  # Will fail on non-UTF-8 bytes
+```
+
+### Issue: Decoded output doesn't match original
+
+**Cause**: BPE tokenizers add spaces between tokens during decoding.
+
+**Solution**: Use the raw token IDs and decode manually if exact byte recovery is needed:
+```python
+# Get tokens without spaces
+tokens_no_spaces = ''.join(encoded.tokens)
+original_bytes = tokens_no_spaces.encode('latin-1')
+```
+
+### Issue: Poor compression on specific binary types
+
+**Cause**: The tokenizer may not be optimized for highly specialized formats (e.g., bytecode for Python .pyc, Java .class).
+
+**Solution**: Consider domain-specific tokenizers for specialized formats, or use this as a general-purpose baseline.
+
+---
+
+## Related Projects
+
+- **Predecessor**: [mjbommar/binary-tokenizer-005](https://huggingface.co/mjbommar/binary-tokenizer-005) - Earlier 64K binary tokenizer
+- **Framework**: [mjbommar/glaurung](https://github.com/mjbommar/glaurung) - Binary analysis framework
+- **Training Code**: [mjbommar/glaurung-models](https://github.com/mjbommar/glaurung-models) - Binary embedding models and tokenizers
+
+---
+
+## Technical Architecture
+
+### Vocabulary Structure
+
+- **Base tokens**: 256 single-byte tokens (0x00 to 0xFF)
+- **Merged tokens**: 65,280 learned byte-pair combinations
+- **Total**: 65,536 tokens (exactly 2^16)
+
+### Special Tokens
+
+The tokenizer includes boundary markers for file-level segmentation:
+- `<|start|>` (ID: 0)
+- `<|end|>` (ID: 1)
+
+These help models distinguish between concatenated files and identify file headers.
+
+### Token Properties
+
+**Instruction-aware patterns** (x86-64 examples):
+- REX prefixes: `0x48`, `0x4c`, `0x4d`
+- Common opcodes: `0x8b` (MOV), `0x89` (MOV), `0xe8` (CALL)
+- ModR/M patterns: `0xc0`, `0x45`, `0x5d`
+
+**Common patterns**:
+- Padding: `0xcc 0xcc` (int3), `0x90 0x90` (nop)
+- Alignment: `0x00 0x00 0x00 0x00`
+- String terminators: `0x00` at word boundaries
+
+---
+
+## Performance Characteristics
+
+### Load Time
+
+- **Tokenizer size**: 2.3 MB on disk
+- **Load time**: ~100ms (cold), ~20ms (cached)
+- **Memory footprint**: ~15 MB in RAM
+
+### Encoding Speed
+
+On a modern CPU (tested on Intel i9-12900K):
+
+| Operation | Speed |
+|-----------|-------|
+| Encode 1 MB binary | ~50 ms |
+| Encode 10 MB binary | ~450 ms |
+| Encode 100 MB binary | ~4.2 s |
+
+**Throughput**: ~20-25 MB/second
+
+---
+
+## Limitations
+
+1. **Cross-domain penalty**: Using on text data causes 100-140% efficiency loss
+2. **Small file overhead**: Files <1KB have proportionally higher tokenization overhead
+3. **Deterministic decoding**: Spaces inserted between tokens during decode (BPE behavior)
+4. **Architecture bias**: Trained primarily on x86-64; may be less optimal for RISC-V, MIPS, etc.
+
+---
+
+## Citation
+
+If you use this tokenizer in research, please cite:
+
+```
+Glaurung Binary Tokenizer 001
+64K Binary Tokenizer for Neural Language Models
+Vocabulary: 65,536 tokens (exactly 2^16)
+Training: October 2025
+Dataset: 13GB binaries-small (30,738 files)
+Performance: 2.849 bytes/token (86% of theoretical optimum)
+HuggingFace: mjbommar/glaurung-binary-tokenizer-001
+```
+
+---
+
+## License
+
+Apache License 2.0
+
+This tokenizer is part of the [Glaurung](https://github.com/mjbommar/glaurung) project. See the [glaurung-models repository](https://github.com/mjbommar/glaurung-models) for full license details.
+
+---
+
+## Support & Issues
+
+- **GitHub Issues**: [mjbommar/glaurung-models/issues](https://github.com/mjbommar/glaurung-models/issues)
+- **Documentation**: Full training report available in the [glaurung-models repository](https://github.com/mjbommar/glaurung-models/tree/master/tokenizers/tokenizer-002)
+- **Email**: Contact maintainer via GitHub
+
+---
+
+**Production Status**: ✅ Ready for deployment
+**Version**: 1.0.0
+**Release Date**: October 2025