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8f7ef44 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 | """JEV-2 Inference Engine with Typed Decision Primitives (Noul, Choice, Score)
Compatible with VTXAI/VTX-JEV-2 and VTXAI/VTX-MOD-1.
Usage:
from inference import JevClient, Choice, Noul, Score
client = JevClient.from_pretrained("VTXAI/VTX-JEV-2")
response = client.system_one(
state="I was charged twice and production is unavailable.",
questions={
"refund": Noul("Does the customer request a refund?"),
"team": Choice(
"Which team should handle this?",
{"billing": "Payments", "technical": "Production outage"},
),
"severity": Score(
"How severe is the impact?",
["Minor", "Major", "Critical"],
),
},
)
print(response.nouls["refund"].noul) # bool (e.g. True)
print(response.choices["team"].choice) # key (e.g. 'billing')
print(response.scores["severity"].score) # top label or expected rank
print(response.to_dict())
"""
from __future__ import annotations
import os
import json
import time
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Union, Any, Optional
import numpy as np
from safetensors.numpy import load_file
from tokenizers import Tokenizer
from huggingface_hub import hf_hub_download
# --- Decision Primitives ---
class Noul:
"""Binary decision / Gating condition (Yes / No)."""
def __init__(self, question: str, positive_anchor: str = "yes positive true confirm affirmative request", negative_anchor: str = "no negative false ignore denial safe", threshold: float = 0.5):
self.question = question
self.positive_anchor = positive_anchor
self.negative_anchor = negative_anchor
self.threshold = threshold
class Choice:
"""Categorical routing / Selection among options."""
def __init__(self, question: str, options: Union[List[str], Dict[str, str]]):
self.question = question
if isinstance(options, list):
self.options = {opt: opt for opt in options}
else:
self.options = options
class Score:
"""Ordinal priority / Severity calibration."""
def __init__(self, question: str, scale: List[str]):
self.question = question
self.scale = scale
# --- Response Containers ---
@dataclass
class NoulResult:
noul: bool
probability: float
confidence: float
def to_dict(self) -> Dict[str, Any]:
return {
"noul": self.noul,
"probability": round(self.probability, 4),
"confidence": round(self.confidence, 4),
}
@dataclass
class ChoiceResult:
choice: str
confidence: float
distribution: Dict[str, float]
def to_dict(self) -> Dict[str, Any]:
return {
"choice": self.choice,
"confidence": round(self.confidence, 4),
"distribution": {k: round(v, 4) for k, v in self.distribution.items()},
}
@dataclass
class ScoreResult:
score: str
expected_index: float
distribution: Dict[str, float]
def to_dict(self) -> Dict[str, Any]:
return {
"score": self.score,
"expected_index": round(self.expected_index, 4),
"distribution": {k: round(v, 4) for k, v in self.distribution.items()},
}
@dataclass
class SystemOneResponse:
nouls: Dict[str, NoulResult] = field(default_factory=dict)
choices: Dict[str, ChoiceResult] = field(default_factory=dict)
scores: Dict[str, ScoreResult] = field(default_factory=dict)
latency_ms: float = 0.0
def to_dict(self) -> Dict[str, Any]:
return {
"nouls": {k: v.to_dict() for k, v in self.nouls.items()},
"choices": {k: v.to_dict() for k, v in self.choices.items()},
"scores": {k: v.to_dict() for k, v in self.scores.items()},
"latency_ms": round(self.latency_ms, 3),
}
# --- JevClient Core ---
class JevClient:
"""JEV-2 Ultra-fast System 1 Decision & Routing Client."""
def __init__(self, weights: np.ndarray, tokenizer: Tokenizer, config: dict):
self.weights = weights # (V, D) normalized token embeddings
self.tokenizer = tokenizer
self.config = config
self.dim = weights.shape[1]
self.vocab_size = weights.shape[0]
@classmethod
def from_pretrained(cls, model_name_or_path: str) -> "JevClient":
"""Load JevClient from local folder or Hugging Face Hub."""
path = Path(model_name_or_path)
if path.exists() and (path / "model.safetensors").exists():
model_file = str(path / "model.safetensors")
tok_file = str(path / "tokenizer.json")
cfg_file = str(path / "config.json") if (path / "config.json").exists() else None
else:
model_file = hf_hub_download(model_name_or_path, "model.safetensors")
tok_file = hf_hub_download(model_name_or_path, "tokenizer.json")
try:
cfg_file = hf_hub_download(model_name_or_path, "config.json")
except Exception:
cfg_file = None
tensors = load_file(model_file)
weight_key = "embeddings" if "embeddings" in tensors else list(tensors.keys())[0]
weights = tensors[weight_key].astype(np.float32)
# Normalize token embedding table once
norms = np.linalg.norm(weights, axis=-1, keepdims=True)
weights = weights / np.maximum(norms, 1e-12)
tokenizer = Tokenizer.from_file(tok_file)
cfg = json.loads(Path(cfg_file).read_text()) if cfg_file else {}
return cls(weights, tokenizer, cfg)
def encode(self, texts: List[str]) -> np.ndarray:
"""Fast vectorized token lookup and mean pooling on CPU."""
encoded = self.tokenizer.encode_batch(texts)
res = np.zeros((len(texts), self.dim), dtype=np.float32)
for i, item in enumerate(encoded):
ids = [tid for tid in item.ids if 0 <= tid < self.vocab_size]
if ids:
tok_vecs = self.weights[ids]
# Mean pool
pooled = np.mean(tok_vecs, axis=0)
norm = np.linalg.norm(pooled)
if norm > 1e-9:
res[i] = pooled / norm
else:
res[i] = 0.0
return res
def system_one(self, state: str, questions: Dict[str, Union[Noul, Choice, Score]]) -> SystemOneResponse:
"""Execute typed System 1 non-autoregressive decision primitives concurrently."""
t0 = time.perf_counter()
response = SystemOneResponse()
# Gather texts to encode in a single optimized pass
batch_texts = [state]
query_map = {}
for key, q in questions.items():
if isinstance(q, Noul):
# Combined query, positive anchor, negative anchor
idx_q = len(batch_texts)
batch_texts.append(f"{q.question} {state}")
idx_pos = len(batch_texts)
batch_texts.append(f"{q.question} {q.positive_anchor}")
idx_neg = len(batch_texts)
batch_texts.append(f"{q.question} {q.negative_anchor}")
query_map[key] = ("noul", q, (idx_q, idx_pos, idx_neg))
elif isinstance(q, Choice):
idx_start = len(batch_texts)
keys = list(q.options.keys())
for k in keys:
batch_texts.append(f"{q.question} {q.options[k]}")
query_map[key] = ("choice", q, (idx_start, keys))
elif isinstance(q, Score):
idx_start = len(batch_texts)
for opt in q.scale:
batch_texts.append(f"{q.question} {opt}")
query_map[key] = ("score", q, (idx_start, q.scale))
# Single batch CPU encode
all_vecs = self.encode(batch_texts)
state_vec = all_vecs[0]
# Process each primitive result
for key, info in query_map.items():
q_type = info[0]
if q_type == "noul":
_, q, (idx_q, idx_pos, idx_neg) = info
q_vec = all_vecs[idx_q]
pos_vec = all_vecs[idx_pos]
neg_vec = all_vecs[idx_neg]
sim_pos = float(np.dot(q_vec, pos_vec))
sim_neg = float(np.dot(q_vec, neg_vec))
diff = sim_pos - sim_neg
prob = float(1.0 / (1.0 + np.exp(-diff * 12.0)))
decision = bool(prob >= q.threshold)
conf = float(prob if decision else (1.0 - prob))
response.nouls[key] = NoulResult(noul=decision, probability=prob, confidence=conf)
elif q_type == "choice":
_, q, (idx_start, keys) = info
cand_vecs = all_vecs[idx_start : idx_start + len(keys)]
# Affinities with state and question context
sims = np.dot(cand_vecs, state_vec)
# Softmax with temperature
exp_s = np.exp(sims * 15.0)
probs = exp_s / np.sum(exp_s)
best_idx = int(np.argmax(probs))
response.choices[key] = ChoiceResult(
choice=keys[best_idx],
confidence=float(probs[best_idx]),
distribution={k: float(p) for k, p in zip(keys, probs)},
)
elif q_type == "score":
_, q, (idx_start, scale) = info
cand_vecs = all_vecs[idx_start : idx_start + len(scale)]
sims = np.dot(cand_vecs, state_vec)
exp_s = np.exp(sims * 15.0)
probs = exp_s / np.sum(exp_s)
best_idx = int(np.argmax(probs))
exp_idx = float(np.sum(np.arange(len(scale)) * probs))
response.scores[key] = ScoreResult(
score=scale[best_idx],
expected_index=exp_idx,
distribution={k: float(p) for k, p in zip(scale, probs)},
)
response.latency_ms = (time.perf_counter() - t0) * 1000
return response
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