Upload binary-tokenizer-001-4k tokenizer

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README.md

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+---
+language:
+- code
+tags:
+- tokenizer
+- binary-analysis
+- binary-tokenization
+- bpe
+- byte-pair-encoding
+- reverse-engineering
+- malware-analysis
+- cybersecurity
+- executable-analysis
+license: mit
+pipeline_tag: feature-extraction
+library_name: tokenizers
+---
+
+# binary-tokenizer-001-4k
+
+**Model Name**: `binary-tokenizer-001-4k`
+**HuggingFace**: [`mjbommar/binary-tokenizer-001-4k`](https://huggingface.co/mjbommar/binary-tokenizer-001-4k)
+**Vocabulary Size**: 4,096 tokens (2^12)
+
+---
+
+## Training Configuration
+
+**Training Corpus**:
+- Source: `/nas4/data/glaurung-data/binaries-small/`
+- Size: ~13 GB
+- Files: 30,738 binary files
+- Platforms: Linux (ELF), Windows (PE), macOS (Mach-O), Android (APK)
+- Architectures: x86-64, x86, ARM64, ARM, MIPS, RISC-V
+
+**Training Parameters**:
+- Vocabulary size: 4,096 (including 7 special tokens)
+- Min frequency: 10
+- Chunk size: 8,192 bytes
+- Allowed lengths: DEFAULT (1-16 bytes)
+- Training duration: ~1h 46min
+
+---
+
+## Vocabulary Statistics
+
+**Composition**:
+- Base bytes (0-255): 256 tokens
+- Learned merges: 3,833 tokens
+- Special tokens: 7 tokens (`<|start|>`, `<|end|>`, `<|pad|>`, `<|unk|>`, `<|cls|>`, `<|sep|>`, `<|mask|>`)
+- **Total**: 4,096 tokens
+
+**Quality Metrics**:
+- All tokens reachable: ✓ Yes
+- Valid merges: 3,833 / 3,833
+- Power-of-2 size: ✓ Yes (2^12)
+
+---
+
+## Token Length Distribution
+
+| Length | Count | Percentage | Description |
+|--------|-------|------------|-------------|
+| 1 byte | 256 | 6.3% | Base bytes |
+| 2 bytes | 1,974 | 48.3% | Byte pairs |
+| 3 bytes | 841 | 20.6% | Complete x86-64 instructions |
+| 4 bytes | 649 | 15.9% | Instructions with operands |
+| 5 bytes | 95 | 2.3% | Complex patterns |
+| 6 bytes | 86 | 2.1% | Complex patterns |
+| 7 bytes | 40 | 1.0% | Complex patterns |
+| 8 bytes | 59 | 1.4% | Complex patterns |
+| 9+ bytes | 89 | 2.2% | Long patterns |
+
+**Average Token Length**: 3.000 bytes
+
+---
+
+## Byte Content Analysis
+
+**Content Categories**:
+- Contains NULL byte (0x00): 1,094 tokens (26.7%)
+- ASCII printable (0x20-0x7E): 896 tokens (21.9%)
+- All ASCII (<0x80): 1,879 tokens (45.9%)
+- High bytes (≥0x80): 2,210 tokens (54.0%)
+
+**Most Common Bytes in Tokens**:
+- `0x00` (NULL): 2,468 occurrences - Padding and alignment
+- `0xFF`: 404 occurrences - Sentinel values
+- `0x48` (REX.W): 340 occurrences - x86-64 REX prefix
+- `0x8B` (MOV): 233 occurrences - x86-64 MOV opcode
+- `0xCC` (INT3): 170 occurrences - Debug breakpoint padding
+
+---
+
+## Sequence Coverage
+
+**N-byte Sequence Diversity**:
+| Length | Learned Tokens | Possible Sequences | Coverage |
+|--------|----------------|-------------------|----------|
+| 1-byte | 256 | 256 | 100.00% |
+| 2-byte | 1,974 | 65,536 | 3.01% |
+| 3-byte | 841 | 16,777,216 | 0.005% |
+| 4-byte | 649 | 4,294,967,296 | 0.000015% |
+
+---
+
+## Files
+
+- `tokenizer-4096.json` - Trained tokenizer model (286 KB)
+- `analysis_results.json` - Detailed analysis statistics
+- `training.log` - Training output log
+- `training_stats.txt` - Training summary
+
+---
+
+## Usage
+
+**Load from HuggingFace Hub**:
+```python
+from tokenizers import Tokenizer
+
+# Load directly from HuggingFace
+tokenizer = Tokenizer.from_pretrained("mjbommar/binary-tokenizer-001-4k")
+```
+
+**Load from local file**:
+```bash
+# With bbpe CLI
+bbpe encode --tokenizer tokenizer-4096.json /path/to/binary
+bbpe info tokenizer-4096.json
+```
+
+**Complete Python Example**:
+```python
+from tokenizers import Tokenizer
+
+# Load from HuggingFace or local file
+tokenizer = Tokenizer.from_pretrained("mjbommar/binary-tokenizer-001-4k")
+# OR: tokenizer = Tokenizer.from_file("tokenizer-4096.json")
+
+# Read binary file and decode as latin-1 (preserves all byte values 0-255)
+with open("/usr/bin/ls", "rb") as f:
+    data = f.read()
+    data_str = data.decode("latin-1")
+
+# Encode the binary data
+encoding = tokenizer.encode(data_str)
+print(f"File size: {len(data)} bytes")
+print(f"Total tokens: {len(encoding.ids)}")
+print(f"Compression: {len(data) / len(encoding.ids):.3f} bytes/token")
+
+# First 10 tokens
+for i, (token_id, token) in enumerate(zip(encoding.ids[:10], encoding.tokens[:10])):
+    token_bytes = token.encode("latin-1")
+    print(f"  Token {i}: ID={token_id:5d} hex={token_bytes.hex():20s} ({len(token_bytes)} bytes)")
+
+# Decode tokens back to bytes
+decoded_str = tokenizer.decode(encoding.ids)
+decoded_bytes = decoded_str.encode("latin-1")
+assert decoded_bytes == data  # Perfect reconstruction
+```
+
+**Example output for `/usr/bin/ls` (142,312 bytes)**:
+```
+File size: 142312 bytes
+Total tokens: 71272
+Compression: 1.997 bytes/token
+
+First 10 tokens:
+  Token 0: ID=  127 hex=7f                   (1 bytes)
+  Token 1: ID= 3732 hex=454c                 (2 bytes)
+  Token 2: ID=   70 hex=46                   (1 bytes)
+  Token 3: ID=    2 hex=02                   (1 bytes)
+  Token 4: ID=  392 hex=0101                 (2 bytes)
+  Token 5: ID=  662 hex=000000000000000000   (9 bytes)
+  Token 6: ID=  265 hex=0300                 (2 bytes)
+  Token 7: ID= 1369 hex=3e00                 (2 bytes)
+  Token 8: ID=  279 hex=01000000             (4 bytes)
+  Token 9: ID=   48 hex=30                   (1 bytes)
+
+Decoded: 7f454c4602010100000000000000000003003e000100000030...
+(ELF header: 7f 45 4c 46 = ELF magic bytes)
+```
+
+---
+
+**Generated**: November 12, 2025
+**Training Script**: `train_tokenizers.sh`
+**Analysis Script**: `analyze_tokenizer.py`

analysis_results.json

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+{
+  "vocab_size": {
+    "total": 4089,
+    "total_with_special": 4096,
+    "base": 256,
+    "merges": 3833,
+    "special": 7,
+    "is_power_of_2": true,
+    "power": 12,
+    "matches_expected": true
+  },
+  "reachability": {
+    "valid_merges": 3833,
+    "invalid_merges": 0,
+    "reachable": 4089,
+    "unreachable": 0,
+    "all_reachable": true
+  },
+  "length_dist": {
+    "distribution": {
+      "1": 256,
+      "2": 1974,
+      "3": 841,
+      "4": 649,
+      "5": 95,
+      "6": 86,
+      "7": 40,
+      "8": 59,
+      "9": 19,
+      "10": 11,
+      "11": 7,
+      "12": 15,
+      "13": 3,
+      "14": 7,
+      "15": 5,
+      "16": 11,
+      "17": 2,
+      "19": 1,
+      "21": 1,
+      "23": 1,
+      "32": 5,
+      "20": 1
+    },
+    "avg_length": 3.0004891171435557,
+    "min_length": 1,
+    "max_length": 32,
+    "length_3_count": 841,
+    "length_3_percent": 20.56737588652482
+  },
+  "byte_content": {
+    "null_tokens": 1094,
+    "ascii_printable": 896,
+    "ascii_only": 1879,
+    "high_byte": 2210,
+    "mixed": 965,
+    "byte_distribution": {
+      "0": 2468,
+      "255": 404,
+      "72": 340,
+      "1": 287,
+      "32": 251,
+      "3": 235,
+      "139": 233,
+      "204": 170,
+      "36": 160,
+      "64": 159,
+      "2": 155,
+      "116": 155,
+      "65": 148,
+      "249": 144,
+      "128": 123,
+      "4": 122,
+      "101": 122,
+      "137": 121,
+      "15": 118,
+      "145": 103,
+      "97": 93,
+      "8": 92,
+      "68": 91,
+      "131": 88,
+      "232": 87,
+      "114": 87,
+      "16": 83,
+      "170": 80,
+      "110": 79,
+      "111": 78,
+      "105": 77,
+      "84": 75,
+      "115": 75,
+      "169": 72,
+      "192": 71,
+      "99": 70,
+      "117": 68,
+      "141": 68,
+      "6": 67,
+      "76": 66,
+      "69": 66,
+      "108": 66,
+      "31": 65,
+      "5": 61,
+      "33": 60,
+      "112": 59,
+      "100": 58,
+      "48": 57,
+      "224": 57,
+      "95": 57
+    }
+  },
+  "diversity": {
+    "1": {
+      "learned": 256,
+      "possible": 256,
+      "coverage": 100.0
+    },
+    "2": {
+      "learned": 1974,
+      "possible": 65536,
+      "coverage": 3.0120849609375
+    },
+    "3": {
+      "learned": 841,
+      "possible": 16777216,
+      "coverage": 0.0050127506256103516
+    },
+    "4": {
+      "learned": 649,
+      "possible": 4294967296,
+      "coverage": 1.5110708773136139e-05
+    }
+  }
+}