File size: 7,933 Bytes
eb50150 | 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 | """ARBITER standalone inference client (copy this file, no package to install).
Mirrors the exact training/eval prompt format from System One (`s1/schema.py`,
`s1/engine.py`): <state> tags with 70/30 head-tail truncation, `Question (kind):`
labels, `(A) option — description` lines, and a single logit readout at the
`Answer: (` slot. Deviating from this format degrades scores — do not rephrase it.
Requires: torch, transformers. Weights: Qwen3-0.6B-Base fine-tune (Apache-2.0).
Usage:
from arbiter import Arbiter
arb = Arbiter("Utiric/arbiter-general") # or a local directory
out = arb.decide(
state="Package never arrived, I want a refund.",
questions=[{"id": "intent", "type": "choice",
"instructions": "What does the customer want?",
"options": {"Refund": "wants money back",
"Whereabouts": "asks where the package is"}}],
)
# {"intent": {"choice": "Refund", "probabilities": {...}, "confidence": 0.61}}
"""
import json
import string
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
LETTERS = list(string.ascii_uppercase) + list(string.ascii_lowercase) # 52 slots
KIND_TAG = {"choice": "choice", "noul": "yes/no", "score": "score"}
TRUNC_MARKER = "\n[... truncated ...]\n"
def _truncate(tok, text, max_tokens):
ids = tok.encode(text, add_special_tokens=False)
if len(ids) <= max_tokens:
return ids
head = int(max_tokens * 0.7)
tail = max_tokens - head - 8
mid = tok.encode(TRUNC_MARKER, add_special_tokens=False)
return ids[:head] + mid + ids[-tail:]
def _render_option(letter, opt, desc):
opt = str(opt).strip()
if desc:
return f"({letter}) {opt} — {str(desc).strip()}"
return f"({letter}) {opt}"
def _build_ids(tok, state, qtext, kind, options, descs, max_state_tokens):
if not isinstance(state, str):
state = json.dumps(state, ensure_ascii=False, indent=1)
bos = [tok.bos_token_id] if tok.bos_token_id is not None else []
ids = list(bos) + tok.encode("<state>\n", add_special_tokens=False)
ids += _truncate(tok, state, max_state_tokens)
ids += tok.encode("\n</state>\n", add_special_tokens=False)
lines = [f"\nQuestion ({KIND_TAG.get(kind, kind)}): {str(qtext).strip()}"]
if kind == "score":
lines.append("\nLevels:")
elif kind == "choice":
lines.append("\nOptions:")
for j, o in enumerate(options):
d = descs[j] if descs and j < len(descs) else None
lines.append("\n" + _render_option(LETTERS[j], o, d))
lines.append("\nAnswer: (")
ids.extend(tok.encode("".join(lines), add_special_tokens=False))
return ids
class Arbiter:
def __init__(self, path, device=None, temperature=None, max_state_tokens=2048):
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.bfloat16 if self.device == "cuda" else torch.float32
self.tok = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
if self.tok.pad_token_id is None:
self.tok.pad_token = self.tok.eos_token or "<|endoftext|>"
self.lm = (
AutoModelForCausalLM.from_pretrained(
path, dtype=dtype, trust_remote_code=True
)
.to(self.device)
.eval()
)
inner = getattr(self.lm, "model", None)
self.backbone = getattr(inner, "language_model", inner) or self.lm
cfg = self.lm.config
self.softcap = getattr(
getattr(cfg, "text_config", cfg), "final_logit_softcapping", None
)
lids = []
for L in LETTERS:
e = self.tok.encode(L, add_special_tokens=False)
assert len(e) == 1, f"letter {L!r} is not a single token in this tokenizer"
lids.append(e[0])
self.letter_ids = torch.tensor(lids, device=self.device)
self.temperature = 1.0 if temperature is None else float(temperature)
try:
import os
cfg_path = os.path.join(path, "s1_config.json")
if os.path.isfile(cfg_path):
with open(cfg_path) as f:
self.temperature = float(
json.load(f).get("temperature", self.temperature)
)
except OSError:
pass
self.max_state_tokens = max_state_tokens
@torch.no_grad()
def _probs(self, ids, nopts):
x = torch.tensor([ids], device=self.device)
mask = torch.ones_like(x)
h = self.backbone(input_ids=x, attention_mask=mask).last_hidden_state[:, -1, :]
W = self.lm.get_output_embeddings().weight[self.letter_ids]
logits = torch.nn.functional.linear(h.to(W.dtype), W).float()
if self.softcap:
logits = torch.tanh(logits / self.softcap) * self.softcap
logits = logits / self.temperature
logits[:, nopts:] = float("-inf")
return torch.softmax(logits[0, :nopts], -1).cpu().tolist()
def decide(self, state, questions):
out = {}
for q in questions:
kind = q.get("type", "choice")
text = q.get("instructions") or q.get("question") or q.get("text") or ""
if kind == "noul":
crit = q.get("criteria") or {}
opts = ["no", "yes"]
descs = (
[crit.get("false"), crit.get("true")]
if isinstance(crit, dict)
else None
)
elif kind == "score":
levels = q.get("levels") or []
opts = [str(i) for i in range(len(levels))]
descs = list(levels)
else:
o = q.get("options") or []
if isinstance(o, dict):
opts, descs = list(o.keys()), list(o.values())
else:
opts, descs = [str(x) for x in o], None
assert len(opts) <= len(LETTERS), f"max {len(LETTERS)} options per pass"
ids = _build_ids(
self.tok, state, text, kind, opts, descs, self.max_state_tokens
)
p = self._probs(ids, len(opts))
by_opt = {o: p[i] for i, o in enumerate(opts)}
top = sorted(range(len(opts)), key=lambda i: -p[i])
conf = p[top[0]] - (p[top[1]] if len(p) > 1 else 0.0)
qid = q.get("id", "q")
if kind == "noul":
out[qid] = {"noul": p[1], "confidence": conf, "probabilities": by_opt}
elif kind == "score":
out[qid] = {
"level": str(top[0]),
"score": sum(i * v for i, v in enumerate(p)),
"confidence": conf,
"probabilities": by_opt,
}
else:
out[qid] = {
"choice": opts[top[0]],
"confidence": conf,
"probabilities": by_opt,
}
return out
if __name__ == "__main__":
import sys
arb = Arbiter(sys.argv[1] if len(sys.argv) > 1 else "Utiric/arbiter-general")
print(
json.dumps(
arb.decide(
state="Kargo 20 gündür gelmedi, iade istiyorum.",
questions=[
{
"id": "intent",
"type": "choice",
"instructions": "What does the customer want?",
"options": {
"Refund": "wants money back",
"Whereabouts": "asks where the package is",
"Cancel": "wants to cancel",
"Greeting": "just saying hello",
},
}
],
),
ensure_ascii=False,
indent=1,
)
)
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