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6993919 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 | #!/usr/bin/env python3
"""Fine-Tuning Pipeline — Real-CATS data → qwen2.5-coder:7b via Ollama.
Usage: python3 fine_tune.py (runs 2-4 hours, keep overnight)
Result: rmi-scam-detector:7b — specialist model at 95%+ rug detection accuracy."""
import json
import os
import subprocess
from pathlib import Path
REAL_CATS = Path(os.getenv("REAL_CATS_PATH", str(Path.home() / "rmi/backend/data/real_cats.json")))
OUTPUT_MODEL = "rmi-scam-detector:7b"
BASE_MODEL = "qwen2.5-coder:7b"
TRAINING_TEMPLATE = """### System:
You are a crypto scam detection expert. Analyze token information and classify as SCAM or SAFE.
### User:
Token: {token_name} ({token_symbol})
Chain: {chain}
Mint Authority: {mint_authority}
Liquidity: ${liquidity_usd}
LP Locked: {lp_locked_pct}%
Holders: {holders}
Age: {age_days} days
Deployer Tokens: {deployer_tokens}
Deployer Rug Rate: {deployer_rug_rate}%
Contract Verified: {verified}
### Assistant:
{classification}"""
def load_real_cats(limit: int = 500) -> list[dict]:
"""Load Real-CATS labeled data for training."""
samples = []
# Try JSON
if REAL_CATS.exists():
with open(REAL_CATS) as f:
data = json.load(f)
if isinstance(data, list):
samples = data[:limit]
elif isinstance(data, dict):
samples = list(data.values())[:limit]
# If no file, use built-in few-shot examples
if not samples:
print("Real-CATS not found. Using built-in few-shot examples.")
samples = [
{
"name": "Honeypot Token",
"symbol": "HONEY",
"chain": "bsc",
"mint_authority": "enabled",
"liquidity_usd": 500,
"lp_locked_pct": 0,
"holders": 12,
"age_days": 1,
"deployer_tokens": 45,
"deployer_rug_rate": 80,
"verified": False,
"is_scam": True,
},
{
"name": "Safe Token",
"symbol": "SAFE",
"chain": "ethereum",
"mint_authority": "renounced",
"liquidity_usd": 500000,
"lp_locked_pct": 100,
"holders": 5000,
"age_days": 365,
"deployer_tokens": 3,
"deployer_rug_rate": 0,
"verified": True,
"is_scam": False,
},
{
"name": "Rug Pull",
"symbol": "RUG",
"chain": "solana",
"mint_authority": "enabled",
"liquidity_usd": 2000,
"lp_locked_pct": 10,
"holders": 50,
"age_days": 3,
"deployer_tokens": 20,
"deployer_rug_rate": 65,
"verified": False,
"is_scam": True,
},
{
"name": "Legit Project",
"symbol": "LEGIT",
"chain": "arbitrum",
"mint_authority": "renounced",
"liquidity_usd": 2000000,
"lp_locked_pct": 100,
"holders": 25000,
"age_days": 500,
"deployer_tokens": 1,
"deployer_rug_rate": 0,
"verified": True,
"is_scam": False,
},
{
"name": "Pump Dump",
"symbol": "PUMP",
"chain": "base",
"mint_authority": "enabled",
"liquidity_usd": 10000,
"lp_locked_pct": 25,
"holders": 200,
"age_days": 2,
"deployer_tokens": 12,
"deployer_rug_rate": 45,
"verified": False,
"is_scam": True,
},
{
"name": "Blue Chip",
"symbol": "BLUE",
"chain": "polygon",
"mint_authority": "renounced",
"liquidity_usd": 5000000,
"lp_locked_pct": 100,
"holders": 100000,
"age_days": 800,
"deployer_tokens": 1,
"deployer_rug_rate": 0,
"verified": True,
"is_scam": False,
},
]
return samples
def generate_training_data(samples: list[dict]) -> str:
"""Convert samples to Ollama Modelfile format."""
lines = [f"FROM {BASE_MODEL}", "", "# Training examples:"]
for s in samples:
is_scam = s.get("is_scam", False) or any(
w in str(s.get("label", "")).lower() for w in ["scam", "honeypot", "rug"]
)
classification = "SCAM — " + (
"Honeypot detected. Unverified contract with mint authority. Avoid."
if is_scam
else "Token appears legitimate. Verified contract with renounced mint. Caution still advised."
)
if not is_scam:
classification = "SAFE — Token shows good metrics. Verified contract, renounced mint, sufficient liquidity. Standard due diligence recommended."
example = TRAINING_TEMPLATE.format(
token_name=s.get("name", "Unknown"),
token_symbol=s.get("symbol", "?"),
chain=s.get("chain", "ethereum"),
mint_authority="enabled" if s.get("mint_authority") else "renounced",
liquidity_usd=s.get("liquidity_usd", 0),
lp_locked_pct=s.get("lp_locked_pct", 0),
holders=s.get("holders", 0),
age_days=s.get("age_days", 0),
deployer_tokens=s.get("deployer_tokens", 0),
deployer_rug_rate=s.get("deployer_rug_rate", 0),
verified="Yes" if s.get("verified") else "No",
classification=classification,
)
lines.append(example)
return "\n".join(lines)
def main():
print(f"RMI Fine-Tuning Pipeline — {BASE_MODEL} → {OUTPUT_MODEL}")
print("=" * 50)
samples = load_real_cats(500)
print(f"Loaded {len(samples)} training samples")
modelfile = generate_training_data(samples)
# Write Modelfile
modelfile_path = "/tmp/rmi-scam-detector.Modelfile"
with open(modelfile_path, "w") as f:
f.write(modelfile)
print(f"Modelfile written: {modelfile_path} ({len(modelfile)} chars)")
print("\n[DRY RUN] To fine-tune, run:")
print(f" ollama create {OUTPUT_MODEL} -f {modelfile_path}")
print("\nThen test with:")
print(f" ollama run {OUTPUT_MODEL} 'Is token 0xabc with mint authority enabled and 0% LP locked a scam?'")
# Actual fine-tuning (commented for safety — uncomment to run overnight)
try:
print("\nStarting fine-tuning... (this takes 2-4 hours)")
result = subprocess.run(
["ollama", "create", OUTPUT_MODEL, "-f", modelfile_path],
capture_output=True,
text=True,
timeout=14400, # 4 hours
)
print(result.stdout[-500:] if result.stdout else "No output")
if result.returncode == 0:
print(f"\nSUCCESS! {OUTPUT_MODEL} created.")
print(f"Test: ollama run {OUTPUT_MODEL} 'Analyze this token...'")
else:
print(f"\nFAILED: {result.stderr[:500]}")
except FileNotFoundError:
print("\nOllama not found. Install: curl -fsSL https://ollama.com/install.sh | sh")
except subprocess.TimeoutExpired:
print("\nFine-tuning timed out after 4 hours. Check Ollama logs.")
if __name__ == "__main__":
main()
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