JPT-35B-A3B

JPT-0.8B · JPT-4B · JPT-9B · JPT-35B-A3B · llm2jev

2048	Snake	Tetris

Each move is one choice question answered in one forward pass (30 moves each, seed 0, SGLang, recorded headless).

What is JPT-35B-A3B

JPT-35B-A3B is the largest JPT: an open decision model that takes a situation and typed questions and returns a calibrated probability for every option from one forward pass. No generated explanation, no reasoning tokens — latency is one prefill, and only 3B of its 35B parameters are active per token.

It implements the typed-decision interface introduced by Jev from TypeSafe AI [1]: a caller sends a state plus questions, and each question is one of three types. JPT is an independent model, not derived from Jev and not trained on Jev outputs; it is an open alternative behind the same interface.

Built on Qwen/Qwen3.5-35B-A3B, a mixture-of-experts model (3B active parameters per token): a LoRA fine-tune merged into full weights, with the same recipe and data as JPT-4B and JPT-9B. The vision tower is unchanged, so screenshots, photos and video frames work next to the text state.

Probabilities use one temperature T = 1.087, fit once on a held-out split — never per benchmark. Read them as well-ordered confidence; ECE varies by task (0.03–0.17).

❯❯ Benchmarks

❯ JevBench v1.4.0

JevBench [2] scores general typed decisions; JPT-35B-A3B gets 206 of the 231 public items, above every system in the v1.4 results. v1.4.0 has 231 public items (frozen since v1.2) and 308 sealed items that only the maintainer can run, so JPT-35B-A3B has no official v1.4 score yet.

By tier: easy 48/48, standard 71/72, hard 87/111. Latency on our runner (tensor parallel 4 on A100 40 GB, no other load on that replica): p50 56 ms, p95 103 ms.

Source: other rows from results/v1.4/jevbench-v1.4-results.json at jevbench commit 2fa63fa (2026-09-23).

❯ Decision Index 0.2.1

Decision Index [3] (0.2.1, 2026-09-27) is the broadest test: 38 scored benchmarks in five areas, chance-corrected. JPT-35B-A3B scores 52.89, 5.8 points above Decider 35B-A3B on the same base model and 6.0 above JPT-9B. We ran the full frozen suite (150,759 requests, every one answered) through llm2jev over SGLang and submitted it as apolinario/decision-index#7; it is not on the live board until the maintainer validates it.

Source: full run and scores.json in kirp/decision-index-results-jpt-35b-a3b (gated: it carries the suite's GPQA/HLE item text). Other rows: live board data/index-v0.2.1.json (generated 2026-09-27 16:59 UTC).

By area, against Jev 1.13.0 on the same items and scorer: JPT-35B-A3B is level with Jev on language and retrieval, behind it on knowledge, tools and arts, and ahead of it on 7 of the 38 index benchmarks.

Chance-corrected skill × 100 (0 = random, 100 = perfect). Jev's numbers are its official entry on the live board (jev-1.13.0); ours are from the same kit and suite.

❯ Against its base model

Same prompt, same serving path, Qwen3.5-35B-A3B zero-shot vs JPT-35B-A3B: fine-tuning lifts every row but ANLI r1, where the strong base is level, and buys +15 points on typed decisions at the cost of calibration there (ECE 0.031 → 0.171).

Banking77, MASSIVE, AG News, Emotion, SST-5 and typed rows are in-distribution (train splits in the mix, test items not). The image evals and EnvBench have not been run on this checkpoint yet.

Jev 1.13.0 has no official score on these splits (our own test/dev cuts, EnvBench, and the image sets), so its column is "—"; its official scores on the Decision Index versions of ANLI and BANKING77 are in the per-benchmark table above. Running the Jev API on these splits would fill them.

❯❯ Quick Start

Two pieces: an engine that holds the weights, and llm2jev (>= 0.6.1) in front of it, reading option probabilities off the engine. The bf16 weights are 66 GB, so below 80 GB per GPU use tensor parallelism.

⚡ SGLang (recommended)

python -m sglang.launch_server --model-path kirp/jpt-35b-a3b --port 30000 --tp 4 --mem-fraction-static 0.7 \
  --context-length 32768 --mamba-scheduler-strategy extra_buffer &  # Qwen3.5's DeltaNet layers need this flag
llm2jev --model kirp/jpt-35b-a3b --backend sglang --url http://127.0.0.1:30000 --port 8080 --temperature 1.087

Tested with SGLang 0.5.18 on 4 × A100 40 GB. --mem-fraction-static 0.7 leaves room for the vision tower's warm-up; at the 0.8 we use for smaller sizes, the warm-up runs out of memory at TP 4.

🔁 vLLM

vllm serve kirp/jpt-35b-a3b --tensor-parallel-size 4 --max-logprobs 256 --return-tokens-as-token-ids \
  --enable-scale-out --port 8000
llm2jev --model kirp/jpt-35b-a3b --backend vllm --url http://127.0.0.1:8000 --port 8080 --temperature 1.087

The three flags after --tensor-parallel-size are required: without them every request is a bare HTTP 400. Not yet tested with this checkpoint.

📨 Ask it a question

import requests
r = requests.post("http://127.0.0.1:8080/v1/systemone", json={
    "state": "Refund policy: full refund within 30 days of purchase; 50% until day 60; none after.\n"
             "Order 1182 was bought on 3 March and returned on 20 April.",
    "questions": {
        "refund": {"type": "choice", "instructions": "What refund does order 1182 get?",
                   "criteria": {"full": "Full refund", "half": "50% refund", "none": "No refund"}},
        "late":   {"type": "noul", "instructions": "Was the return made after day 30?",
                   "criteria": {"true": "Yes", "false": "No"}}}})
print(r.json()["answers"])   # each answer has the per-option probabilities

🖼️ With images

state = [{"role": "user", "content": [
    {"type": "image", "image": "https://example.com/screen.png"},
    {"type": "text", "text": "Task: open the settings page. Numbered boxes mark clickable elements."}]}]
questions = {"click": {"type": "choice", "instructions": "Which box should be clicked?",
                       "criteria": {"1": None, "2": None, "3": None, "4": None, "5": None}}}

❯❯ Training

LoRA with a Brier loss on 49,221 typed questions, one epoch, trained with Megatron-SWIFT and merged into full weights.

The same mix as JPT-4B and JPT-9B (mix_train_env_v11):

• public classification, NLI, QA, preference and safety datasets;
• long legal and contract documents (ContractNLI, MAUD, LegalBench, ConditionalQA, ShARC);
• table and numeric reasoning (TAT-QA, MultiHiertt);
• multi-hop QA (MuSiQue, BEIR);
• agent and tool traces (Mind2Web, AgentTraj, ToolACE);
• community typed-decision sets;
• oracle-labelled rollouts from 20 small game and puzzle environments;
• programmatically generated rule-arithmetic items (dates, time zones, day counts, caps; labels computed by code);
• 1,289 long policy / contract / regulation documents (4,471 questions) written by an LLM (GPT-6 Luna) with no JevBench item shown to it.

❯❯ Limitations

• Arithmetic and dates are its weakest area: it answers from the evidence given and has no reasoning phase by design.
• Overconfident in-distribution. At the global temperature, typed-decisions test ECE is 0.171: confidence on in-distribution typed questions runs above accuracy.
• Up to 255 options are accepted; training covered up to 77 (Banking77), so beyond that quality is not established.
• English first. Other languages come only from a few multilingual classification sets.
• Images are untested on this checkpoint; they go zero-shot through the base vision tower.
• Heavy to host. 66 GB of bf16 weights need several GPUs, even though only 3B parameters are active per token.

❯❯ References

1. TypeSafe AI. Jev. https://typesafe.ai
2. F. Standhartinger. JevBench, v1.4.0. https://github.com/fstandhartinger/jevbench
3. Decision Index, edition 0.2.1. https://huggingface.co/spaces/multimodalart/jev-decision-index
4. Nie et al. Adversarial NLI. ACL 2020.
5. Casanueva et al. Efficient Intent Detection with Dual Sentence Encoders (Banking77). NLP4ConvAI 2020.
6. FitzGerald et al. MASSIVE. ACL 2023.
7. Zhang et al. Character-level Convolutional Networks for Text Classification (AG News). NeurIPS 2015.
8. Saravia et al. CARER: Contextualized Affect Representations for Emotion Recognition. EMNLP 2018.
9. Socher et al. Recursive Deep Models for Semantic Compositionality (SST). EMNLP 2013.
10. EnvBench, v0.1 (ours, frozen 2026-09-23; not yet public): programmatically solved game, planning and rule decisions with exact gold answers.
11. Wu et al. OS-Atlas (ScreenSpot-v2). 2024.
12. Wang et al. Screen2Words. UIST 2021.
13. Gemini Robotics Team. Gemini Robotics (ERQA). 2025.

❯❯ License

CC BY-NC 4.0. The weights derive from Qwen3.5-35B-A3B (Apache-2.0), but some training datasets allow only non-commercial or research use, so the model is released for non-commercial use.

JPT-35B-A3B is an independent open model that implements a typed-decision interface (noul, choice and score questions answered with probabilities). It is not affiliated with, endorsed by or derived from TypeSafe AI or its Jev model, and it was not trained on Jev outputs.