cribl-decision 27B

A parameter-efficient LoRA adapter for structured decision prediction. Given a state, one or more typed questions, and a set of candidate options, cribl-decision returns probability distributions over the supplied candidates.

Note: This is an interim checkpoint (step 16,000 / 48,186, ~33% of training). Evaluation results will be added when a later checkpoint is selected. The adapter is functional but not fully trained.

Evaluation (26-task held-out, interim checkpoint)

Highlights

• LoRA adapter — separate from the 27 B base; swap or freeze independently.
• Structured candidate scoring — probabilities come from projecting the hidden state through the native lm_head, restricted to candidate-letter tokens. No text is generated.
• Three decision types — categorical choice (pick + distribution), noul (Boolean probability), score (ordinal distribution + expected value).
• Up to 255 candidates — labels A–Z, then two-letter single-token labels (AA, AB, …).
• Multi-slot readout — multiple questions packed in one forward pass, each read at its own Answer k: ( slot, conditionally independent.

Quick start

Requirements

pip install torch transformers peft

Loading the adapter

Important: Use Qwen3_5ForConditionalGeneration, not AutoModelForCausalLM. In transformers ≥ 5.19, AutoModelForCausalLM resolves to Qwen3_5ForCausalLM, which silently ignores the LoRA adapter.

import torch
from transformers import AutoTokenizer, Qwen3_5ForConditionalGeneration
from peft import PeftModel

BASE_ID  = "Qwen/Qwen3.5-27B"
BASE_REV = "fc05daec18b0a78c049392ed2e771dde82bdf654"
ADAPTER  = "cribl-ai/cribl-decision-1.0-27B"

tokenizer = AutoTokenizer.from_pretrained(BASE_ID, revision=BASE_REV)
base = Qwen3_5ForConditionalGeneration.from_pretrained(
    BASE_ID, revision=BASE_REV, torch_dtype=torch.bfloat16,
)
model = PeftModel.from_pretrained(base, ADAPTER, is_trainable=False)
model = model.to("cuda").eval()

Prompt format

The model was trained on the following prompt layout. Using a different format will degrade accuracy significantly.

Context:
<state — JSON object, plain text, or any string>

Question: <question text>
Options:
(A) <candidate 1>
(B) <candidate 2>
(C) <candidate 3>
Answer: (

For multiple questions in one pass:

Context:
<state>

Question 1: <text>
Options:
(A) ...
(B) ...

Question 2: <text>
Options:
(A) ...
(B) ...
Answer 1: (
Answer 2: (

Structured readout

The model does not generate text. Instead:

1. Run one forward pass through the transformer (language_model).
2. Take the hidden state at the ( token position (the last token of Answer: ().
3. Gather the lm_head weight rows for the candidate-letter token IDs only.
4. Compute logits = candidate_weights @ hidden_state.
5. Apply softmax to get a probability distribution over the candidates.

# After loading the model as above:
base_model     = model.get_base_model()
language_model = base_model.model.language_model
lm_head        = base_model.lm_head

LETTERS = "ABCDEFGHIJ"

@torch.inference_mode()
def score(prompt, n_options):
    ids = tokenizer.encode(prompt, add_special_tokens=False)
    input_ids = torch.tensor([ids], dtype=torch.long, device="cuda")
    mask = torch.ones_like(input_ids)

    hidden = language_model(input_ids=input_ids, attention_mask=mask).last_hidden_state
    h = hidden[0, -1]  # hidden state at the "(" token

    letter_ids = [tokenizer.encode(LETTERS[i], add_special_tokens=False)[0]
                  for i in range(n_options)]
    cand_ids = torch.tensor(letter_ids, dtype=torch.long, device="cuda")
    weights = lm_head.weight[cand_ids]
    logits = (weights @ h).float()
    probs = torch.softmax(logits, dim=-1).cpu().tolist()
    return probs

Complete example

import json

state = {"message": "My card was charged twice for the same purchase."}
question = "Which department should handle this?"
options = ["billing", "technical support", "sales"]

prompt = (
    f"Context:\n{json.dumps(state)}\n\n"
    f"Question: {question}\n"
    f"Options:\n"
    + "\n".join(f"({LETTERS[i]}) {opt}" for i, opt in enumerate(options))
    + "\nAnswer: ("
)

probs = score(prompt, len(options))
for i, (opt, p) in enumerate(zip(options, probs)):
    print(f"({LETTERS[i]}) {opt:20s}  {p:.4f}")

A standalone runnable script is provided in example_readout.py.

Triton workaround

On some systems (e.g. DGX boxes without Python.h for Triton JIT), the first forward pass may fail with a Triton compilation error. Add this before importing transformers:

from torch._native import triton_utils
triton_utils.deregister_op_overrides()

Decision types

Choice

A categorical pick from the candidate set, plus the full probability distribution.

{
  "type": "choice",
  "pick": "billing",
  "probabilities": {"billing": 0.82, "technical support": 0.14, "sales": 0.04}
}

Noul

A Boolean probability for a yes/no proposition (two candidates: the proposition and its negation).

{
  "type": "noul",
  "probability": 0.91
}

Score

An ordinal distribution over ordered levels, plus the expected score.

{
  "type": "score",
  "distribution": {"low": 0.05, "medium": 0.30, "high": 0.65},
  "expected": 2.60
}

Label table

Every label is guaranteed to be a single token in the Qwen3.5 tokenizer.

Training data

Trained on a broad mixture of public decision datasets spanning classification, intent, preference, safety, knowledge, reasoning, agent/tool, and rule families. Max context length: 8,192 tokens. Max candidates per question: 255. Option order shuffled; large label sets sub-sampled to 10 with the gold always kept. Base transformer frozen; only LoRA parameters were trained.

Limitations

• Interim checkpoint — training is ~33% complete. Accuracy will improve with further training; this release will be updated.
• Can produce incorrect or overconfident decisions.
• Accuracy varies by task family and domain.
• Sensitive to prompt formatting, candidate wording, and option order.
• Generic text generation (standard model.generate()) is not the intended use — use the structured readout described above.
• Calibration should be rechecked on your specific task distribution.

Security and responsible use

• Deployers are responsible for protecting credentials (DECISION_API_TOKEN, SSH keys, .env files) and keeping sensitive data out of logs.
• Validate inputs and monitor calibration in production.
• Add human review for high-impact decisions.

License

Apache-2.0, where compatible with the base-model license. The Qwen3.5-27B model is subject to its own license terms.

Citation

@misc{cribl-decision-27b-2026,
  title        = {cribl-decision 27B: A LoRA Adapter for Structured Decision Prediction},
  author       = {{Cribl AI}},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/cribl-ai/cribl-decision-1.0-27B}}
}