File size: 6,732 Bytes
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import struct
import numpy as np
from transformers import AutoTokenizer
TOKENIZER_DIR = "j:/Language-U/Language-U-V2/qwen-3.5-0.8b-local"
OUTPUT_BIN = "j:/Language-U/qwen_vocab_cuneiform.bin"
def classify_token(token_str):
# Normalize token string (replace special character G/Ġ representing space)
s = token_str.replace("Ġ", " ").replace(" ", "").lower()
# 1. DOMAIN & SUBDOMAIN
domain = 0
subdomain = 0
# Domain 1: Hardware & LoRA Networks
hw_net_kw = ['gpio', 'lora', 'chirp', 'reset', 'pin', 'spi', 'sx1302', 'pi4', 'pi5',
'concentrator', 'antenna', 'frequency', 'mhz', 'dbm', 'packet', 'transmit',
'tx', 'rx', 'sf7', 'fec', 'parity', 'duty', 'dwell', 'mac', 'gateway', 'beacon']
# Domain 2: Mathematics, Logic & Information Theory
math_kw = ['svd', 'dct', 'quant', 'math', 'entropy', 'shannon', 'gradient', 'atom',
'ortho', 'eigen', 'projection', 'reconstruct', 'rank', 'matrix', 'vector',
'basis', 'decompo', 'smooth', 'laplace', 'probability', 'logits', 'prior',
'bypass', 'q_proj', 'v_proj', 'k_proj', 'o_proj']
# Domain 3: Dialogue, Persona & Authors
dialogue_kw = ['zymatica', 'collective', 'dialogue', 'persona', 'chat', 'assistant',
'speak', 'talk', 'bot', 'agent', 'she', 'astronaut', 'devsone', 'bouldiez',
'partner', 'art']
# Domain 4: Software, Systems & Runtimes
sw_kw = ['rust', 'cpp', 'go', 'python', 'swift', 'java', 'typescript', 'compile',
'code', 'exec', 'run', 'lib', 'class', 'struct', 'header', 'import', 'from',
'package', 'build', 'cmake', 'cargo']
if any(k in s for k in hw_net_kw):
domain = 1
if 'lora' in s or 'chirp' in s or 'fec' in s:
subdomain = 1
elif 'gpio' in s or 'pin' in s or 'reset' in s:
subdomain = 2
elif 'packet' in s or 'beacon' in s:
subdomain = 3
elif any(k in s for k in math_kw):
domain = 2
if 'svd' in s or 'matrix' in s or 'projection' in s:
subdomain = 1
elif 'entropy' in s or 'shannon' in s or 'bypass' in s:
subdomain = 2
elif 'logits' in s or 'prior' in s or 'smooth' in s:
subdomain = 3
elif any(k in s for k in dialogue_kw):
domain = 3
if 'zymatica' in s or 'collective' in s:
subdomain = 1
elif 'persona' in s or 'dialogue' in s or 'speak' in s:
subdomain = 2
elif any(k in s for k in sw_kw):
domain = 4
if 'rust' in s or 'go' in s or 'cpp' in s:
subdomain = 1
elif 'python' in s or 'typescript' in s or 'java' in s:
subdomain = 2
# 2. OPERATION (Actions)
# Map key verbs to operation IDs (1 to 15)
operations = [
'reset', 'clear', 'toggle', 'write', 'read', 'set', 'get',
'encode', 'decode', 'compress', 'decompress', 'train', 'heal',
'eval', 'test', 'load', 'save', 'grow', 'shrink', 'bypass'
]
operation = 0
for i, op in enumerate(operations, 1):
if op in s:
operation = i % 16
break
# 3. MODALITY (Data layouts / formats)
modalities = [
'bin', 'zlib', 'json', 'capsule', 'genesis', 'llm', 'file',
'packet', 'byte', 'bit', 'char', 'string', 'token', 'wave', 'hal'
]
modality = 0
for i, mod in enumerate(modalities, 1):
if mod in s:
modality = i % 16
break
# 4. DEPTH (Complexity scale)
depth = 0
if domain == 1:
if 'seed' in s or 'genesis' in s:
depth = 8
elif 'dct' in s:
depth = 4
elif 'atom' in s:
depth = 6
elif 'qa' in s or 'facts' in s:
depth = 1
else:
depth = len(s) % 16
# 5. POLARITY (Outcome states)
polarity = 0
pos_states = ['ack', 'success', 'ok', 'pass', 'valid', 'correct', 'true']
neg_states = ['nack', 'fail', 'error', 'wrong', 'miss', 'warn', 'oom', 'crash', 'abort', 'false']
if any(k in s for k in pos_states):
polarity = 1
elif any(k in s for k in neg_states):
polarity = 2
return domain, subdomain, operation, modality, depth, polarity
def main():
print(f"Loading tokenizer from: {TOKENIZER_DIR}")
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_DIR, trust_remote_code=True)
vocab = tokenizer.get_vocab()
vocab_size = len(vocab)
print(f"Total vocabulary size: {vocab_size}")
# Build the binary map in token ID order
# Each record is 3 bytes: R_C, R_F, R_A
packed_data = bytearray(vocab_size * 3)
domain_counts = {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}
op_counts = 0
polarity_counts = {0: 0, 1: 0, 2: 0}
for token_str, token_id in vocab.items():
if token_id >= vocab_size:
continue
domain, subdomain, operation, modality, depth, polarity = classify_token(token_str)
# Pack into radicals
rc = (domain << 4) | (subdomain & 0xF)
rf = (operation << 4) | (modality & 0xF)
ra = (depth << 4) | (polarity & 0xF)
packed_data[token_id * 3] = rc
packed_data[token_id * 3 + 1] = rf
packed_data[token_id * 3 + 2] = ra
# Metrics collection
domain_counts[domain] = domain_counts.get(domain, 0) + 1
if operation > 0:
op_counts += 1
polarity_counts[polarity] = polarity_counts.get(polarity, 0) + 1
# Write to output file
with open(OUTPUT_BIN, "wb") as f:
f.write(packed_data)
print(f"\n[+] Successfully generated binary map at: {OUTPUT_BIN}")
print(f" Total tokens written: {vocab_size}")
print("\n--- Mapping Classification Metrics ---")
print(f" Domain 0 (General English): {domain_counts[0]:,} tokens ({domain_counts[0]/vocab_size*100:.2f}%)")
print(f" Domain 1 (Hardware & LoRA): {domain_counts[1]:,} tokens ({domain_counts[1]/vocab_size*100:.2f}%)")
print(f" Domain 2 (Math & Info Theory): {domain_counts[2]:,} tokens ({domain_counts[2]/vocab_size*100:.2f}%)")
print(f" Domain 3 (Dialogue & Persona): {domain_counts[3]:,} tokens ({domain_counts[3]/vocab_size*100:.2f}%)")
print(f" Domain 4 (Software & Systems): {domain_counts[4]:,} tokens ({domain_counts[4]/vocab_size*100:.2f}%)")
print(f" Active Operations Detected: {op_counts:,} tokens")
print(f" Neutral Polarity (0): {polarity_counts[0]:,} tokens")
print(f" Positive Polarity (1 - ACK): {polarity_counts[1]:,} tokens")
print(f" Negative Polarity (2 - ERR): {polarity_counts[2]:,} tokens")
if __name__ == "__main__":
main()
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