Hopper

Hopper is a LoRA adapter (rank 16) for Qwen/Qwen3.5-4B at revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a. It is built for the JevBench setting: a document, a policy and a question go in, and a probability distribution over a fixed set of options comes out.

• One forward pass per decision, with thinking off. No text is generated. The answer is a softmax over the logits of the option letters (A, B, C, ...), restricted to as many letters as there are options.
• A calibration map (hopper.json) rescales that distribution by a temperature, T in [1/3, 3]. T is a bounded linear function of what the request shows: the number of options, the state length, whether the state is JSON, the answer type, and the entropy of the model's own distribution. The map never changes the top answer.
• Serving code: github.com/hopit-ai/hopper. It runs an HTTP server with the JevBench /v1/systemone wire format. At load it merges the adapter into the bf16 weights, and it refuses to start if the fast linear-attention kernels are not active.

Code and adapter weights are licensed Apache-2.0. The base model is Apache-2.0 (licence), and this adapter does not change its terms.

Intended use

Hopper makes single-step policy decisions over a short document: yes/no (noul), choice among named options, and ordinal scores. It returns calibrated probabilities, and it is meant to be run and measured on JevBench. It is not a chat model. It is also not meant for decisions with legal, medical, financial or safety consequences unless a person reviews them.

Prompt format

The chat template of Qwen3.5-4B is applied with enable_thinking=False and a generation prompt.

• System: You make decisions about a document under a policy. Read only what is written in the document. Reply with the letter of the correct option and nothing else.
• User: one JSON object, {"evidence": <document>, "criterion": <policy>\n\n<question>, "options": [{"letter": "A", "description": "<label>: <description>"}, ...]}. A yes/no question has the options true and false, and its rubric is appended to the policy. The policy for JevBench items is Decide the case using only what the document states. Exactly one option is correct.

The readout is the next-token logits at the end of the prompt, restricted to the option letters, then a softmax, then the calibration map. hopper_decisions/request.py and prompt.py in the code repository build this prompt exactly.

How to load it

The easiest way to serve it is with the package (pip install the code repository, see its README):

from hopper_decisions import Decider
decider = Decider(adapter="HopitAI/hopper")      # the packaged calibration map is the default
decider.decide({"state": "The customer wants a refund for order 12.",
                "questions": {"decision": {"type": "choice", "instructions": "Route the ticket.",
                                           "criteria": {"refund": "money back", "track": "where is it"}}}})

Or with transformers and peft directly:

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

base, revision = "Qwen/Qwen3.5-4B", "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a"
tokenizer = AutoTokenizer.from_pretrained(base, revision=revision)
model = AutoModelForCausalLM.from_pretrained(base, revision=revision, dtype=torch.bfloat16, device_map="cuda")
model = PeftModel.from_pretrained(model, "HopitAI/hopper").merge_and_unload().eval()

messages = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": user_json}]   # as above
ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=False, return_tensors="pt")
letters = [tokenizer.encode(l, add_special_tokens=False)[0] for l in "AB"]        # one letter per option
with torch.inference_mode():
    logits = model(input_ids=ids.to("cuda")).logits[0, -1, letters].float()
probs = torch.softmax(logits, -1)   # before the calibration map

Qwen3.5's linear-attention layers need flash-linear-attention==0.5.2 and causal-conv1d 1.7.0 to run at full speed. Without them, transformers silently falls back to a path that is more than 10x slower. The pinned versions are torch==2.8.0, transformers==5.17.0, peft==0.21.0 and accelerate==1.15.0.

Training data

The adapter was trained on a mix of three sources:

1. Synthetic decision families made by LLM-based generation. An LLM wrote the families, and their labels were computed in code.
2. A JevBench-style set, also made by LLM-based generation. Items were kept only where independent LLM solvers agreed with the answer. It contains no JevBench item. Every item was checked against all public JevBench questions and states (normalised question identity, and any shared 8-word sequence) and dropped on a match.
3. Public human-labelled datasets. We used examples from their training splits, converted into the decision format above. Each is used under its own licence:

dataset	used for	licence
allenai/ai2_arc (ARC-Challenge, ARC-Easy)	multiple choice	CC BY-SA 4.0
tau/commonsense_qa	multiple choice	MIT
cais/mmlu (auxiliary_train)	multiple choice	MIT (as stated on the dataset card; the auxiliary set collects other public datasets)
stanfordnlp/snli	entailment	CC BY-SA 4.0
nyu-mll/multi_nli	entailment	CC BY 3.0 / CC BY-SA 3.0 / MIT / other, per source genre (see the dataset card)
tals/vitaminc	fact verification	CC BY-SA 3.0
google/boolq	yes/no questions	CC BY-SA 3.0
rajpurkar/squad_v2	answerability	CC BY-SA 4.0
clinc/clinc_oos	intent classification	CC BY 3.0
fancyzhx/dbpedia_14	topic classification	CC BY-SA 3.0
nvidia/HelpSteer2	response-quality judgement	CC BY 4.0

The calibration map was fitted only on our own held-out JevBench-style items. It never saw a JevBench item.

Evaluation

These are local numbers, not official JevBench results. They were computed with our own evaluation path on the 231 public JevBench items (argmax over the exact label set, with ties going to the smallest label as in jevbench/scoring.py). The judge tier and the held-out items are not public, so they are not included. Only the JevBench maintainer's run on his own GPU is official.

tier	items	Hopper	same base, frozen, same prompt and map type
easy	48	1.000	1.000
standard (original)	72	0.944	0.958
hard	111	0.685	0.631

On the hard tier, top-label ECE is 0.102. Distribution fidelity (1 − mean total-variation distance) on the 10 public probability items is 0.830.

Disclosure.

• The public items were split in half before we started. The half we developed on (115 items) was used as a development gate many times: 26 distinct model and prompt configurations, plus more than twenty calibration-map variants. Our JevBench-style training set's style sheet was written by reading that half, and some of its training items target behaviours we saw fail on its hard items. On that half the adapter scores hard 0.709.
• The other half (116) was kept as a reserve and scored only in aggregate. Our models were predicted on it in three earlier sessions (other configurations) and once for this system, chosen beforehand by a pre-registered rule. On it the adapter scores hard 0.661 (37 of 56) and the frozen base 0.643 (36 of 56): it is level with the frozen model on accuracy there, not ahead.
• The calibration map was fitted only on our own held-out JevBench-style items, never on a JevBench item.
• No JevBench item or paraphrase was used in training. Every item we wrote was checked against all public JevBench questions and states (normalised question identity, and any shared 8-word sequence) and dropped on a match; the check reads only hashes and reports only counts.
• Expect the held-out hard items to score below the public ones, and expect the judge tier, which we have never seen, to be the least predictable part.

Limitations

• One pass of a 4B model. Hopper does not reason step by step. Any problem that needs a chain of intermediate results is decided in one forward pass by Qwen3.5-4B.
• Dates and multi-step arithmetic are weak. Date differences, deadlines and chained calculations fail often.
• Long documents that need several hops are weak. Accuracy drops when the answer needs facts from several distant parts of a long document.
• The calibration was fitted on our own data. The map was fitted on our own JevBench-style items. On a different distribution of questions, its confidences can be off.
• The dev half flatters it. On the reserved half of the public items, it is level with the frozen base model on hard-tier accuracy (see the disclosure).
• Tested only on English. We have not measured any other language.