---
title: KnowLine-4B-Gen5.5
canonical_url: "https://www.modelscope.ai/models/EMLab/KnowLine-4B-Gen5.5"
md_url: "https://www.modelscope.ai/models/EMLab/KnowLine-4B-Gen5.5.md"
repository: EMLab/KnowLine-4B-Gen5.5
last_updated: 2026-11-13
license: apache-2.0
pipeline_tag: text-classification
tasks:
  - text-classification
model_type:
  - qwen3_5
architectures:
  - Qwen3_5ForConditionalGeneration
base_model:
  - Qwen/Qwen3.5-4B
base_model_relation: finetune
parameters: 4.7B
tensor_type:
  - BF16
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
  - zh
downloads: 21
stars: 0
tags:
  - decision-model
  - system-one
  - decision-index
  - lora-merged
---

# KnowLine-4B-Gen5.5

> KnowLine-4B-Gen5.5 - An open-source model by EMLab on ModelScope. KnowLine-4B-Gen5 with a new way of serving it: the same weights, and a front end that reads short requests twice before it answers.

EMLab/KnowLine-4B-Gen5.5 is a 4.7B-parameter text-classification model on ModelScope. licensed under apache-2.0. derived from Qwen/Qwen3.5-4B.

- **Repository**: EMLab/KnowLine-4B-Gen5.5
- **License**: apache-2.0
- **Tasks**: text-classification
- **Parameters**: 4.7B
- **Base model**: Qwen/Qwen3.5-4B
- **Tags**: decision-model, system-one, decision-index, lora-merged
- **Downloads**: 21
- **Stars**: 0
- **Last updated**: 2026-11-13

Source: https://www.modelscope.ai/models/EMLab/KnowLine-4B-Gen5.5

---

# KnowLine-4B-Gen5.5

**KnowLine-4B-Gen5 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-4B-Gen5](https://huggingface.co/PelaAI/KnowLine-4B-Gen5) · also:
[KnowLine-35B-A3B-Gen1.5](https://huggingface.co/PelaAI/KnowLine-35B-A3B-Gen1.5),
[KnowLine-26B-A4B-Gen2.5](https://huggingface.co/PelaAI/KnowLine-26B-A4B-Gen2.5)

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 Gen5.5 is byte-identical to Gen5.

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 Gen5.5](#what-changed-in-gen55).
- **Decision Index 0.3 public suite: 66.75** (self-run, fresh full run), up from 65.67 for Gen5 with the same weights.
  The gains are mostly in reasoning and inference over long inputs (knowledge and reasoning 45.0 → 47.3,
  language 66.8 → 68.2); tools and agents are unchanged (87.4) and arts and taste slightly lower (61.2 → 61.0). See [Comparison](#comparison).
- **Private scores:** the board's full score comes from the maintainers' private run. Gen5 is listed at 53.68. Gen5.5 has
  the same weights as Gen5; its private scores will be produced by the maintainers' run.
- **Other public benchmarks:** C-Eval 80.5 (Gen5 on the same files: 79.2) and JevBench / JevBench-hard 85.3 / 70.3
  (83.1 / 66.7).
- **Calibration:** the temperature folded into the weights is unchanged (T = 1.50). Computed with the board's method, ECE is
  about 0.035-0.046 (Gen5: 0.037-0.049) and the Brier score about 0.27-0.30 (Gen5: 0.28-0.31); see [Calibration](#calibration).
- **Cost:** about 1.56 times the prompt tokens of Gen5 on the Decision Index suite. One request at a time on one H20:
  42.1 ms median and 83 ms mean, against 31.6 ms and 58.8 ms for Gen5 (our measurement, not the board's
  reference hardware); see [INFERENCE.md](INFERENCE.md).
- **Images: unchanged.** Requests with images are sent exactly as in Gen5, so Gen5's image results apply; see
  [Images](#images-zero-shot).

KnowLine is an independent model. It is not affiliated with, endorsed by, or 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 earlier versions only need to change the model
  name, because the interface is unchanged.

| | |
|---|---|
| Base model | [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) (Apache-2.0) |
| Weights | byte-identical to [KnowLine-4B-Gen5](https://huggingface.co/PelaAI/KnowLine-4B-Gen5): a LoRA SFT model (rank 32, alpha 64, language model only), merged into bf16 weights, with a readout temperature T = 1.50, fitted on held-out data, folded into the final norm weight |
| Serving | `serve_knowline.sh`: SGLang with FP8 at load, as for Gen5; `knowline_server.py` reads short requests twice (on by default; `--no-read-twice` gives the Gen5 prompts). 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-4B-Gen5.5 0 8080     # SGLang (FP8 at load) 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 Gen5.5

1. **Read twice.** Gen5 saw each request once: the state, then the instruction, the question and its options. Gen5.5
   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 (94.6% of the Decision Index requests); longer requests and requests with images are sent as in
   Gen5.
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.
3. **No training.** The weights, the temperature and the label-token readout are those of Gen5. 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) and
   [KnowLine-26B-A4B-Gen2.5](https://huggingface.co/PelaAI/KnowLine-26B-A4B-Gen2.5).
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. Gen5.5 has no board entry yet.

| model | size | DI 0.3 public | DI 0.4.1 full | source |
|---|---|---|---|---|
| **KnowLine-4B-Gen5.5** | 4B | **66.75** | not yet scored | self-run, official kit |
| KnowLine-4B-Gen5 (same weights) | 4B | 65.67 | 53.68 | board |
| StartLux-Decision v1.1 4B | 4.7B | 67.81 | 54.80 | board |
| Perplexity Decider Flash v1.2 | 4.5B | 60.06 | 54.29 | board |
| vLLM-SR d3-mini | 4.5B | 54.90 | 49.97 | board |
| OneJev 1.6 | 4.5B | 61.95 | 48.88 | board |
| Jev 1.14 | (API) | 60.70 | 60.24 | board |
| KnowLine-35B-A3B-Gen1 | 35B-A3B | 69.49 | 61.20 | 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 | Gen5.5 | Gen5 |
|---|---|---|
| Knowledge & reasoning | 47.3 | 45.0 |
| Language | 68.2 | 66.8 |
| Retrieval & routing | 74.0 | 73.3 |
| Tools & agents | 87.4 | 87.4 |
| Arts & taste | 61.0 | 61.2 |

## Releases

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

| model | date | DI 0.3 public | notes |
|---|---|---|---|
| KnowLine-4B-Gen5.5 (this model) | 2026-10-22 | 66.75 | Gen5 weights, read-twice serving |
| [KnowLine-4B-Gen5](https://huggingface.co/PelaAI/KnowLine-4B-Gen5) | 2026-10-21 | 65.67 | fifth release |
| [KnowLine-4B-Gen4](https://huggingface.co/PelaAI/KnowLine-4B-Gen4) | 2026-10-10 | 64.90 | fourth release |
| [KnowLine-4B-Gen3](https://huggingface.co/PelaAI/KnowLine-4B-Gen3) | 2026-10-09 | 63.11 | third release |
| [KnowLine-4B-Gen2](https://huggingface.co/PelaAI/KnowLine-4B-Gen2) | 2026-10-08 | 62.54 | second release |
| [KnowLine-4B-Gen1](https://huggingface.co/PelaAI/KnowLine-4B-Gen1) | 2026-10-07 | 60.47 | first release |

## Evaluation

All results are self-run and have not been verified by a third party. Every Gen5.5 number below was measured through the
Gen5.5 front end (read twice on).

### Other public benchmarks

| suite | Gen5.5 | Gen5 |
|---|---|---|
| C-Eval (4 categories, macro) | 80.5 | 79.2 |
| JevBench / JevBench-hard | 85.3 / 70.3 | 83.1 / 66.7 |

- In a paired test against Gen5, p = 0.02 for C-Eval; the differences on JevBench (p = 0.23) and JevBench-hard
  (p = 0.34) are not significant.

### Web operation (Mind2Web official test splits, evaluation only)

| split | Gen5.5 element selection | Gen5.5 operation (balanced) | Gen5 element selection | Gen5 operation (balanced) |
|---|---|---|---|---|
| test_task (Cross-Task) | 92.7 | 96.9 | 92.6 | 97.1 |
| test_website (Cross-Website) | 91.2 | 96.3 | 90.9 | 96.9 |
| test_domain (Cross-Domain) | 92.0 | 98.0 | 91.8 | 98.4 |

- Within noise of Gen5 on all three splits (paired test, p ≥ 0.06); 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 Gen5:** read twice applies only to text-only requests. A request with images is sent exactly as in
  Gen5, byte for byte, to the same weights, so Gen5's image behaviour holds for Gen5.5.
- **No image training:** the vision tower is the base model's and image use is zero-shot; see the
  [Gen5 card](https://huggingface.co/PelaAI/KnowLine-4B-Gen5#images-zero-shot).

### 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 | Gen5.5 | Gen5 |
|---|---|---|
| ECE (lower is better) | 0.035-0.046 | 0.037-0.049 |
| Brier score (lower is better) | 0.27-0.30 | 0.28-0.31 |
| Confidence ≥95% but wrong | 1.5-1.6% | 1.5-1.6% |
| Mean confidence / accuracy | 0.83 / 0.78-0.80 | 0.82-0.83 / 0.78-0.80 |

- **Temperature in the weights:** unchanged from Gen5: the final norm weight includes T = 1.50 (fitted on held-out data), so every logit is divided
  by 1.50 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 Gen5.
- **Recommendation:** if your use depends on probability thresholds, check calibration on your own data.

## Disclosures

- **Training data:** the weights are those of Gen5, so Gen5's disclosures on training data and decontamination apply
  unchanged; see the [Gen5 card](https://huggingface.co/PelaAI/KnowLine-4B-Gen5#disclosures).
- **Public suite:** the Decision Index public suite is public; the board's private tests may show a lower score.

## Limitations

- **Slower than Gen5:** about 1.56 times the prompt tokens on the Decision Index suite; one request at a time on one
  H20, 42.1 ms median and 83 ms mean (Gen5: 31.6 ms and 58.8 ms), and 297 ms at the 95th
  percentile (Gen5: 144 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 Gen5.
- **Not better everywhere:** arts and taste on the public suite is slightly lower than with Gen5 (61.2 → 61.0).
- **Everything else as Gen5:** knowledge-heavy reasoning is still the weakest area and a 4B model is limited there; the
  board's private new-domain tasks favour larger models; prompt-injection attacks may still work; image use is
  zero-shot.

## Citation

```bibtex
@misc{knowline4bgen55,
  title  = {KnowLine-4B-Gen5.5: a 4B decision model served with read twice},
  author = {PelaAI},
  year   = {2026},
  url    = {https://huggingface.co/PelaAI/KnowLine-4B-Gen5.5}
}
```

## 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).