Text Classification
Transformers
Safetensors
English
Chinese
qwen3_5
image-text-to-text
decision-model
system-one
decision-index
lora-merged
Instructions to use PelaAI/KnowLine-4B-Gen1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PelaAI/KnowLine-4B-Gen1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PelaAI/KnowLine-4B-Gen1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("PelaAI/KnowLine-4B-Gen1") model = AutoModelForMultimodalLM.from_pretrained("PelaAI/KnowLine-4B-Gen1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
README.md: link KnowLine-4B-Gen2, add DI 0.3 calibration
Browse files
README.md
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[Inference guide](INFERENCE.md) · [中文说明](README.zh.md) · weights Apache-2.0
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Highlights:
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- **Decision Index 0.3, public suite: 60.47** (self-run). For comparison: Jev 1.13 scores 57.96 (board), and the best
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| model | date | DI 0.3 public | DI 0.2.1 | golden held-out (en / zh-Hans / zh-Hant) | notes |
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| KnowLine-4B-Gen1 (this model) | 2026-10-07 | 60.47 | 60.92 | 68.7 / 69.9 / 64.1 | first release (internal run "mix E", step 5,650) |
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## Evaluation
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- **Prompt injection:** an instruction planted in the state changes the answer about 9% of the time on our injection
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set. Keep untrusted text clearly delimited.
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- **Calibration:** ECE is about 0.05-0.06 on choice and yes/no questions and about 0.12 on score questions. For score
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questions, fit a temperature on your own data.
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- **Private tests:** part of our public-suite advantage comes from adapting to the question formats, so the 0.3 private
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tests may score lower.
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[Inference guide](INFERENCE.md) · [中文说明](README.zh.md) · weights Apache-2.0
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> **Newer version:** [KnowLine-4B-Gen2](https://huggingface.co/PelaAI/KnowLine-4B-Gen2) is out, with a Decision
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> Index 0.3 public score of 62.54, and at or above this model on all of our own evaluations.
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Highlights:
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- **Decision Index 0.3, public suite: 60.47** (self-run). For comparison: Jev 1.13 scores 57.96 (board), and the best
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| model | date | DI 0.3 public | DI 0.2.1 | golden held-out (en / zh-Hans / zh-Hant) | notes |
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| [KnowLine-4B-Gen2](https://huggingface.co/PelaAI/KnowLine-4B-Gen2) | 2026-10-08 | 62.54 | — | 69.7 / 71.2 / 64.8 | this model at 0.5 + mix F steps 4,500 and 5,000 at 0.25 each, weight average |
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| KnowLine-4B-Gen1 (this model) | 2026-10-07 | 60.47 | 60.92 | 68.7 / 69.9 / 64.1 | first release (internal run "mix E", step 5,650) |
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## Evaluation
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- **Prompt injection:** an instruction planted in the state changes the answer about 9% of the time on our injection
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set. Keep untrusted text clearly delimited.
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- **Calibration:** ECE is about 0.05-0.06 on choice and yes/no questions and about 0.12 on score questions. For score
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questions, fit a temperature on your own data. On the Decision Index 0.3 public suite, computed the board's way, ECE
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is about 0.08-0.09 and 3.3% of answers are wrong at ≥95% confidence (board median 1.3%): the model is overconfident
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overall.
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- **Private tests:** part of our public-suite advantage comes from adapting to the question formats, so the 0.3 private
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tests may score lower.
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