| """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) |
| 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, |
| ) |
| ) |
|
|
