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SecGPT-7B-GGUF

clouditera

我们基于 **Qwen2.5-Instruct 系列** 与 **DeepSeek-R1 系列** 模型，结合自建安全任务集与安全知识库，在 **8台A100 GPU** 集群上持续训练一周以上，完成大规模预训练 + 指令微调 + 强化学习，显著提升模型在安全场景中的**理解、推理与响应能力**。我们已构建了一个超大规模、结构完备的网络安全语料库，总量超过 **5TB**、共计 **106,721 个原始文件**，其中超过 **40% 内容为人工精选与结构化处理**。私有数据部分系统整合了具备 **70+ 字段 / 14 类结构标签体系** 的安全数据资源，经过统一清洗、语义标注与重构，构建出 **数百亿 Tokens 级**的高质量语料，为大模型深度推理能力提供坚实支撑。为全面评估 SecGPT 的安全实战能力，我们构建了一套覆盖**安全证书问答、安全通识、编程能力、知识理解与推理能力**的综合评估体系，主要采用以下标准化数据集：CISSP、CS-EVAL、CEVAL、GSM8K、BBH。

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Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

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How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

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SecGPT-7B-GGUF

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