KnowLine-26B-A4B-Gen2.5

KnowLine-26B-A4B-Gen2 with a new way of serving it: the same weights, and a front end that reads short requests twice before it answers.

Inference guide · 中文说明 · weights Apache-2.0 · previous version: KnowLine-26B-A4B-Gen2 · sibling: KnowLine-35B-A3B-Gen1.5

We raise the minor version when only inference changes and the major version (Gen) when the weights change. Every weight, config and tokenizer file of Gen2.5 is byte-identical to Gen2.

Highlights:

Same weights, read twice. For a text-only request of up to 3,000 tokens, the front end writes the whole input a second time before the model answers. Nothing was trained; see What changed in Gen2.5.
Decision Index 0.3 public suite: 68.21 (self-run, fresh full run), up from 66.94 for Gen2 with the same weights. The gains are concentrated in reasoning and inference over long inputs (knowledge and reasoning 51.9 → 55.1, language 69.7 → 71.2); tools and agents are slightly lower (84.4 → 83.3). See Comparison.
Private scores: the board's full score comes from the maintainers' private run. Gen2 is listed at 61.34. Gen2.5 has the same weights as Gen2; its private scores will be produced by the maintainers' run.
Other public benchmarks: C-Eval 77.9 (Gen2: 74.8), JevBench / JevBench-hard 90.5 / 81.1 (88.3 / 76.6); see Evaluation.
Calibration: the temperature folded into the weights, fitted on held-out data, is unchanged (T = 1.25). Computed with the board's method, ECE is about 0.013-0.018 (Gen2: 0.008-0.012), slightly higher, and the Brier score about 0.24-0.27 (Gen2: 0.26-0.28); see Calibration.
Cost: about 1.54 times the prompt tokens of Gen2 on the Decision Index suite. One request at a time on one H20: 66.3 ms median and 151 ms mean, against 43.1 ms and 82.8 ms for Gen2 (our measurement, not the board's reference hardware); see INFERENCE.md.
Images: unchanged. Requests with images are sent exactly as in Gen2, so Gen2's image results apply; see Images.

KnowLine is an independent model. It is not affiliated with or endorsed by TypeSafe, Jev or Google, and it is not derived from TypeSafe or Jev.

What it does

You send a state (text or a chat) and up to 64 typed questions: yes/no, choose one of k, or score on a rubric. The model answers every question with a probability distribution over its options:

one forward pass per question, with no text generation;
the output is the probability of each option's label token;
existing Jev clients only need to change the base URL, and users of other KnowLine models only need to change the model name, because the interface is the same.


Base model	google/gemma-4-26B-A4B-it (Apache-2.0): mixture of experts, about 26B parameters in total and about 4B active per token
Weights	byte-identical to KnowLine-26B-A4B-Gen2: LoRA SFT (rank 32, alpha 64, language model only, including the routed experts), merged into bf16 weights (about 52 GB), with a readout temperature T = 1.25 folded into the final norm weight and the final logit soft-cap
Serving	`serve_knowline.sh`: SGLang in bf16, as for Gen2 (no FP8 at load for this model); `knowline_server.py` reads short requests twice (on by default; `--no-read-twice` gives the Gen2 prompts). One 80 GB GPU is enough; we ran every evaluation with one server on one 96 GB H20.
Languages	English, Simplified Chinese, Traditional Chinese

Quickstart

pip install "sglang==0.5.21" "transformers==5.12.1" requests
bash serve_knowline.sh PelaAI/KnowLine-26B-A4B-Gen2.5 0 8080   # SGLang (bf16) on :9080 + /v1/systemone on :8080
curl -s http://127.0.0.1:8080/v1/systemone -H 'Content-Type: application/json' -d '{
  "model": "m",
  "state": "Customer: my order arrived broken, I want my money back.",
  "questions": {"refund": {"type": "noul", "instructions": "Should the agent offer a refund?"},
                "tone": {"type": "choice", "instructions": "Customer tone?",
                         "criteria": {"angry": "Angry", "neutral": "Neutral", "happy": "Happy"}}}}'

Front end: knowline_server.py with knowline_layout.py next to it; together they depend only on transformers and requests.
Full settings: the exact settings used for our Decision Index run are in INFERENCE.md.

What changed in Gen2.5

Read twice. Gen2 saw each request once: the state, then the instruction, the question and its options. Gen2.5 writes all of that a second time, introduced by the line "Read the same input again before answering:", and then reads the answer from the label token as before. The rule is mechanical: it applies to every text-only request of at most 3,000 tokens (94.1% of the Decision Index requests); longer requests and requests with images are sent as in Gen2.
A likely reason. With causal attention, the first copy of a long state is read before the model knows the question; in the second copy every token can also attend to the first copy, question included. This fits where the gains are: reasoning and inference over long inputs.
No training. The weights, the temperature and the label-token readout are those of Gen2. We checked the change on a fresh full run of the Decision Index public suite before release. It is the same change as in KnowLine-35B-A3B-Gen1.5.
Credit. The read-twice layout follows FlexDM (github.com/1000luca/FlexDM, MIT); repeating the input goes back to RE2 (Xu et al., 2023, arXiv:2309.06275). knowline_layout.py is our own implementation.

Comparison

Scores on the Decision Index. Since Decision Index 0.4, the board's full score is 60% private tests of the same skills and 40% private tasks from new domains; the public suite is shown but no longer counts. Gen2.5 has no board entry yet.

model	size	DI 0.3 public	DI 0.4.1 full	source
KnowLine-26B-A4B-Gen2.5	26B-A4B	68.21	not yet scored	self-run, official kit
KnowLine-26B-A4B-Gen2 (same weights)	26B-A4B	66.94	61.34	board
KnowLine-35B-A3B-Gen1.5	35B-A3B	70.72	not yet scored	self-run, official kit
KnowLine-35B-A3B-Gen1	35B-A3B	69.49	61.20	board
Fastino GLiDE V1.2	–	71.99	71.52	board
Jeffan-31b-1710	31B	73.13	69.75	board
mammoth-1	27B	75.18	68.87	board
Perplexity Decider v1.2	27B	67.74	67.98	board
StartLux-Decision v1.1 27B	27B	72.88	67.92	board
FlexDM · Gemma-4-31B	31B	63.63	67.56	board
Jev 1.14	(API)	60.70	60.24	board

Board rows from the board's data of 2026-10-21 (22:34 UTC, Decision Index 0.4.1).

Public-suite skill by area (DI 0.3, same weights):

area	Gen2.5	Gen2
Knowledge & reasoning	55.1	51.9
Language	71.2	69.7
Retrieval & routing	70.9	70.4
Tools & agents	83.3	84.4
Arts & taste	61.4	60.0

Releases

The model is trained in a self-evolving loop, and new versions will follow.

model	date	DI 0.3 public	notes
KnowLine-26B-A4B-Gen2.5 (this model)	2026-10-22	68.21	Gen2 weights, read-twice serving
KnowLine-35B-A3B-Gen1.5	2026-10-22	70.72	35B-A3B Gen1 weights, read-twice serving
KnowLine-4B-Gen5	2026-10-21	65.67	4B, fifth release
KnowLine-26B-A4B-Gen2	2026-10-19	66.94	second 26B-A4B release
KnowLine-26B-A4B-Gen1	2026-10-19	65.76	first release on a Gemma 4 base
KnowLine-35B-A3B-Gen1	2026-10-12	69.49	first 35B-A3B release

Evaluation

All results are self-run and have not been verified by a third party. Every Gen2.5 number below was measured through the Gen2.5 front end (read twice on). Unless noted, the Gen2 numbers were measured again on the same files.

Other public benchmarks

suite	26B-A4B-Gen2.5	26B-A4B-Gen2
C-Eval (4 categories, macro)	77.9	74.8
JevBench / JevBench-hard	90.5 / 81.1	88.3 / 76.6

In a paired test against Gen2, p < 0.001 for C-Eval, p = 0.23 for JevBench and p = 0.18 for JevBench-hard.
The Gen2 column matches the Gen2 card.

Web operation (Mind2Web official test splits)

split	Gen2.5 element selection	Gen2.5 operation (balanced)	Gen2 element selection	Gen2 operation (balanced)
test_task (Cross-Task)	89.8	90.3	89.5	91.6
test_website (Cross-Website)	89.2	94.0	88.1	94.1
test_domain (Cross-Domain)	89.6	92.9	88.8	93.4

Element selection is slightly higher than with Gen2 on all three splits (paired test p = 0.04 on test_domain, p ≥ 0.17 on the other two); operation accuracy is within noise of Gen2 (p ≥ 0.18). No request failed.
The task is to pick the target element from a candidate set: the target plus up to 5 other elements sampled from the page. This is easier than the original Mind2Web protocol, so do not compare these results with the Mind2Web leaderboard.

Images (zero-shot)

Unchanged from Gen2: read twice applies only to text-only requests. A request with images is sent exactly as in Gen2, byte for byte, to the same weights, so Gen2's image results hold for Gen2.5 and we did not rerun them.
Gen2's result: 63.3 on the official vision public suite (self-run); see the Gen2 card for the per-benchmark table.

Calibration

Computed on the 0.3 public suite using the method described by the Decision Index board: each field is scored right or wrong, confidence is the probability of the chosen option, benchmarks are weighted equally, and 10 equal-width bins are used.

metric	26B-A4B-Gen2.5	26B-A4B-Gen2
ECE (lower is better)	0.013-0.018	0.008-0.012
Brier score (lower is better)	0.24-0.27	0.26-0.28
Confidence ≥95% but wrong	0.9%	0.8-0.9%
Mean confidence / accuracy	0.82-0.83 / 0.80-0.82	0.80-0.81 / 0.79-0.81

Temperature in the weights: unchanged from Gen2: the final norm weight and the final logit soft-cap include T = 1.25, so every final logit is divided by 1.25 before the softmax over the option labels.
Why ranges: the board does not publish which benchmarks it uses for calibration, so we report ranges over the plausible benchmark sets, as for Gen2.
Recommendation: if your use depends on probability thresholds, check calibration on your own data.

Disclosures

Training data: the weights are those of Gen2, so the decontamination statement on the Gen2 card applies unchanged. The training data is not included in this repository.
Public suite: the Decision Index public suite is public; the board's private tests may show a lower score.

Limitations

Slower than Gen2: about 1.54 times the prompt tokens on the Decision Index suite; one request at a time on one H20, 66.3 ms median and 151 ms mean (Gen2: 43.1 ms and 82.8 ms), and 629 ms at the 95th percentile (Gen2: 238 ms). Use --no-read-twice if latency matters more than these gains.
Only short requests change: text-only requests over 3,000 tokens, and requests with images, are answered exactly as by Gen2.
Not better everywhere: a few benchmarks, including some tool and agent tasks, are slightly lower than with Gen2.
Everything else as Gen2: Chinese is still clearly below the 35B-A3B (C-Eval); web operation and images are below the 35B-A3B; prompt-injection attacks may still work, so put untrusted content in data fields of the state, not in the instructions; the board's private tests may show a lower score than the public suite.

Citation

@misc{knowline26ba4bgen25,
  title  = {KnowLine-26B-A4B-Gen2.5: a 26B-A4B decision model served with read twice},
  author = {PelaAI},
  year   = {2026},
  url    = {https://huggingface.co/PelaAI/KnowLine-26B-A4B-Gen2.5}
}

License

Weights: Apache-2.0, the same as the base model.
Code: knowline_server.py, knowline_layout.py and knowline_engine.py are MIT (see the file headers).