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探索 AI 模型 8

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**This adversarial model has a propensity to produce highly unsavoury content from the outset. It is not intended or suitable for general use or human consumption.** This special-use model aims to provide prompts that goad LLMs into producting "toxicity". Toxicity here is defined by the content of the Civil Comments dataset, containing categories such as `obscene`, `threat`, `insult`, `identity_attack`, `sexual_explicit` and `severe_toxicity`. For details, see the description of the Jigsaw 2019 data. The base model is the community version of gpt2 with ~125M parameters. This model is not aligned and is "noisy" relative to more advanced models. Both the lack of alignment and the existence of noise are favourable to the task of trying to goad other models into producing unsafe output: unsafe prompts have a propensity to yield unsafe outputs, and noisy behaviour can lead to a broader exploration of input space.

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Garak Refusal Detector is a binary sequence classifier model that detects refusal responses in LLM outputs. The model is built as a semantic replacement for string-based keyword detectors (e.g., Garak's MitigationBypass detector), enabling refusal detection based on meaning rather than surface patterns. Developers integrating the Garak framework can use this model to detect refusal responses in LLM outputs. It serves as an alternative to keyword‑based detectors, using a transformer‑based classifier that returns a binary signal indicating refusal or non‑refusal. Garak Refusal Detector is a fine-tuned version of answerdotai/ModernBERT-base, trained on synthetic refusal and non-refusal data for binary sequence classification.

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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They're prompt results of OpenAI gpt-3.5-turbo run on June 1 2023. Prompts are constructed by prepending "Explain why" to claims made in the True-False dataset provided alongside The Internal State of an LLM Knows When its Lying. Only the first sentence of the response is included (from nltk.sent_tokenize). The original labels are used, where 0 corresponds to a false claim. That is, the 0 labels should be almost all refutations. Spurious and missing refutations were removed by hand, corresponding to about 10% of the data. This were caused by either an incorrect model assertion, or errors in the source data.

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This model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by **Jigsaw** (Jigsaw 2018, Jigsaw 2019, Jigsaw 2020), containing around 2 million examples. We split it into two parts and fine-tune a RoBERTa model (RoBERTa: A Robustly Optimized BERT Pretraining Approach) on it. The classifiers perform closely on the test set of the first Jigsaw competition, reaching the **AUC-ROC** of 0.98 and **F1-score** of 0.76. This model is licensed under the OpenRAIL++ License, which supports the development of various technologies—both industrial and academic—that serve the public good.

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This model is intended to use for classify toxic online classifications. However, one limitation of the model is that it performs poorly for some comments that mention a specific identity subgroup, like Muslim. The following table shows a evaluation score for different identity group. You can learn the specific meaning of this metrics here. But basically, those metrics shows how well a model performs for a specific group. The larger the number, the better. The table above shows that the model performs poorly for the muslim and jewish group. In fact, you pass the sentence "Muslims are people who follow or practice Islam, an Abrahamic monotheistic religion." Into the model, the model will classify it as toxic. Be mindful for this type of potential bias. The training data comes this Kaggle competition. We use 10% of the `train.csv` data to train the model.

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

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选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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