| """Jegue 0.2: typed decisions (choice / yes-no) with calibrated probabilities. |
| |
| Two classes, same call: response, _ = model(state, questions) or response = model.ask(state, questions) |
| |
| Jegue(mode="direct") Hugging Face / PyTorch. One forward pass per request, no text generated. mode="routed" adds the learned router: questions it flags get a stock-model |
| rationale (at most 64 tokens, generated with the adapter off) before the answer slot is read. |
| JegueVLLM(mode="routed") vLLM (one instance, CUDA graphs, prefix cache, the LoRA served by vLLM). The fast path; use it for latency. mode: routed | direct | brief. |
| |
| `state` is a string (the shared context); `questions` is a dict: {"id": {"type": "choice", "instructions": "...", "criteria": {"key": "description", ...}}} or |
| {"id": {"type": "noul", "instructions": "..."}} (yes/no, returns the probability of yes). The response is {"answers": {"id": {"type": "choice", "choice": key, "probabilities": {...}}}}. |
| """ |
| import os |
| import sys |
|
|
| HERE = os.path.dirname(os.path.abspath(__file__)) |
| sys.path.insert(0, HERE) |
| ROOT = os.path.dirname(HERE) |
| WEIGHTS = os.path.join(ROOT, "weights") |
|
|
|
|
| class Jegue: |
| """Hugging Face path. mode: 'direct' (single pass, default) or 'routed'.""" |
|
|
| def __new__(cls, mode="direct", model=ROOT, weights=WEIGHTS, **options): |
| if mode == "routed": |
| from decision_engine_v6r import RoutedSlotEngine |
| eng = RoutedSlotEngine(model=model, run=weights, ckpt=weights, parallel_context="direct", **options) |
| elif mode == "direct": |
| from decision_engine_v6 import BriefSlotEngine |
| eng = BriefSlotEngine(model=model, run=weights, ckpt=weights, context="direct", parallel_context="direct", temperature="none", **options) |
| else: |
| raise ValueError("mode must be 'direct' or 'routed'") |
| eng.ask = lambda state, questions: eng(state, questions)[0] |
| return eng |
|
|
|
|
| class JegueVLLM: |
| """vLLM path (needs `pip install vllm`, one CUDA GPU). mode: 'routed' (default), 'direct' or 'brief'.""" |
|
|
| def __new__(cls, mode="routed", model=ROOT, weights=WEIGHTS, **options): |
| from decision_engine_vr import VllmRoutedEngine |
| eng = VllmRoutedEngine(model=model, run=weights, ckpt=weights, mode=mode, **options) |
| eng.ask = lambda state, questions: eng(state, questions)[0] |
| return eng |
|
|
