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@@ -54,7 +54,7 @@ Built for [RhythmAI](https://github.com/SyedCode01) -- an AI-powered SOC platfor
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  | **LoRA Dropout** | 0.05 |
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  | **Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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  | **Training Precision** | 4-bit QLoRA with BF16 compute |
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- | **Context Length** | 4,096 tokens |
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  | **GGUF Format** | MXFP4 (13 GB) |
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  | **License** | Apache 2.0 (inherited from GPT-OSS) |
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@@ -192,7 +192,7 @@ This model is designed for cybersecurity professionals, SOC analysts, and securi
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  - **Domain-specific**: Optimized for cybersecurity tasks; general knowledge may be less reliable than the base model
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  - **Not a replacement for human analysts**: Outputs should be validated by qualified security professionals
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  - **Training data bias**: Performance may vary for threats or attack patterns not well-represented in the training data
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- - **Context window**: Limited to 4,096 tokens; very long log files or reports may need to be chunked
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  - **No real-time data**: The model does not have access to real-time threat intelligence feeds
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  ## Citation
@@ -200,7 +200,7 @@ This model is designed for cybersecurity professionals, SOC analysts, and securi
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  ```bibtex
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  @misc{rhythmai-cybersec-20b,
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  title={RhythmAI Cybersec 20B: A Fine-Tuned Cybersecurity Language Model},
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- author={Syed Hasan Rizvi},
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  year={2026},
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  url={https://huggingface.co/SyedCode01/rhythmai-cybersec-20b},
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  note={Fine-tuned from OpenAI GPT-OSS-20B for SOC operations}
 
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  | **LoRA Dropout** | 0.05 |
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  | **Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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  | **Training Precision** | 4-bit QLoRA with BF16 compute |
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+ | **Context Length** | 65,536 tokens (64K) |
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  | **GGUF Format** | MXFP4 (13 GB) |
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  | **License** | Apache 2.0 (inherited from GPT-OSS) |
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  - **Domain-specific**: Optimized for cybersecurity tasks; general knowledge may be less reliable than the base model
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  - **Not a replacement for human analysts**: Outputs should be validated by qualified security professionals
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  - **Training data bias**: Performance may vary for threats or attack patterns not well-represented in the training data
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+ - **Context window**: Supports up to 65,536 tokens (64K); training used 4,096 max sequence length but the base model's full context capability is preserved
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  - **No real-time data**: The model does not have access to real-time threat intelligence feeds
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  ## Citation
 
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  ```bibtex
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  @misc{rhythmai-cybersec-20b,
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  title={RhythmAI Cybersec 20B: A Fine-Tuned Cybersecurity Language Model},
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+ author={Syed Hasan Iqbal},
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  year={2026},
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  url={https://huggingface.co/SyedCode01/rhythmai-cybersec-20b},
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  note={Fine-tuned from OpenAI GPT-OSS-20B for SOC operations}