---
title: KnowLine-26B-A4B-Gen2.5
canonical_url: "https://www.modelscope.ai/models/EMLab/KnowLine-26B-A4B-Gen2.5"
md_url: "https://www.modelscope.ai/models/EMLab/KnowLine-26B-A4B-Gen2.5.md"
repository: EMLab/KnowLine-26B-A4B-Gen2.5
last_updated: 2026-11-13
license: apache-2.0
pipeline_tag: text-classification
tasks:
  - text-classification
model_type:
  - gemma4
architectures:
  - Gemma4ForConditionalGeneration
base_model:
  - google/gemma-4-26B-A4B-it
base_model_relation: finetune
parameters: 25.8B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
  - zh
downloads: 15
stars: 0
tags:
  - decision-model
  - system-one
  - decision-index
  - lora-merged
  - moe
---

# KnowLine-26B-A4B-Gen2.5

> KnowLine-26B-A4B-Gen2.5 - An open-source model by EMLab on ModelScope. KnowLine-26B-A4B-Gen2.5

EMLab/KnowLine-26B-A4B-Gen2.5 is a 25.8B-parameter text-classification model on ModelScope. licensed under apache-2.0. derived from google/gemma-4-26B-A4B-it.

- **Repository**: EMLab/KnowLine-26B-A4B-Gen2.5
- **License**: apache-2.0
- **Tasks**: text-classification
- **Parameters**: 25.8B
- **Base model**: google/gemma-4-26B-A4B-it
- **Tags**: decision-model, system-one, decision-index, lora-merged, moe
- **Downloads**: 15
- **Stars**: 0
- **Last updated**: 2026-11-13

Source: https://www.modelscope.ai/models/EMLab/KnowLine-26B-A4B-Gen2.5

---

# 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](INFERENCE.md) · [中文说明](README.zh.md) · weights Apache-2.0 · previous version:
[KnowLine-26B-A4B-Gen2](https://huggingface.co/PelaAI/KnowLine-26B-A4B-Gen2) · sibling:
[KnowLine-35B-A3B-Gen1.5](https://huggingface.co/PelaAI/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](#what-changed-in-gen25).
- **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](#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](#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](#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](INFERENCE.md).
- **Images: unchanged.** Requests with images are sent exactly as in Gen2, so Gen2's image results apply; see
  [Images](#images-zero-shot).

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](https://huggingface.co/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](https://huggingface.co/PelaAI/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

```bash
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](INFERENCE.md).

## What changed in Gen2.5

1. **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.
2. **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.
3. **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](https://huggingface.co/PelaAI/KnowLine-35B-A3B-Gen1.5).
4. **Credit.** The read-twice layout follows FlexDM ([github.com/1000luca/FlexDM](https://github.com/1000luca/FlexDM),
   MIT); repeating the input goes back to RE2 (Xu et al., 2023,
   [arXiv:2309.06275](https://arxiv.org/abs/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](https://huggingface.co/PelaAI/KnowLine-35B-A3B-Gen1.5) | 2026-10-22 | 70.72 | 35B-A3B Gen1 weights, read-twice serving |
| [KnowLine-4B-Gen5](https://huggingface.co/PelaAI/KnowLine-4B-Gen5) | 2026-10-21 | 65.67 | 4B, fifth release |
| [KnowLine-26B-A4B-Gen2](https://huggingface.co/PelaAI/KnowLine-26B-A4B-Gen2) | 2026-10-19 | 66.94 | second 26B-A4B release |
| [KnowLine-26B-A4B-Gen1](https://huggingface.co/PelaAI/KnowLine-26B-A4B-Gen1) | 2026-10-19 | 65.76 | first release on a Gemma 4 base |
| [KnowLine-35B-A3B-Gen1](https://huggingface.co/PelaAI/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](https://huggingface.co/PelaAI/KnowLine-26B-A4B-Gen2#images-zero-shot) 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](https://huggingface.co/PelaAI/KnowLine-26B-A4B-Gen2#disclosures) 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

```bibtex
@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).