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Model: trendmicro-ailab/Llama-Primus-Reasoning Source: Original Platform
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README.md
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---
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license: mit
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datasets:
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- trendmicro-ailab/Primus-Reasoning
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- trendmicro-ailab/Primus-Seed
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- trendmicro-ailab/Primus-FineWeb
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- trendmicro-ailab/Primus-Instruct
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language:
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- en
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base_model:
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- trendmicro-ailab/Llama-Primus-Merged
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- cybersecurity
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- pretraining
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extra_gated_fields:
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Affiliation: text
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Country: country
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I want to use this model for:
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type: select
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options:
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- Research
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- Commercial
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- label: Other
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value: other
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Job title:
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type: select
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options:
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- Student
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- Research graduate
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- AI researcher
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- AI developer/engineer
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- Cybersecurity researcher
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- Reporter
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- Other
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geo: ip_location
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---
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# Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training
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<img src="https://i.imgur.com/PtqeTZw.png" alt="Primus Overview" width="60%">
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**First cybersecurity reasoning model!**
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>TL;DR: Llama-Primus-Reasoning is a reasoning model distilled from the reasoning steps with reflection data generated by o1-preview & DeepSeek-R1 on cybersecurity tasks (_Primus-Reasoning_), based on Llama-Primus-Merged. It demonstrates a 🚀**15.8%** improvement in security certification (CISSP).
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**🔥 For more details, please refer to the paper: [[📄Paper]](https://arxiv.org/abs/2502.11191).**
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**📢 News (2025/06/02)**: We have expanded the [Primus-Reasoning](https://huggingface.co/datasets/trendmicro-ailab/Primus-Reasoning) dataset with additional samples from DeepSeek-R1. Accordingly, we have replaced Llama-Primus-Reasoning with a new version distilled jointly from DeepSeek-R1 and o1-preview. This version achieves the best CISSP performance, with a 15.8% improvement.
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## Introduction
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Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, with promising applications in specialized domains such as finance, law, and biomedicine. However, in the domain of cybersecurity, we noticed a lack of open-source datasets specifically designed for LLM pre-training—even though much research has shown that LLMs acquire their knowledge during pre-training. To fill this gap, we present a collection of datasets covering multiple stages of cybersecurity LLM training, including pre-training (_Primus-Seed_ and _Primus-FineWeb_), instruction fine-tuning (_Primus-Instruct_), and reasoning data for distillation (_Primus-Reasoning_). Based on these datasets and Llama-3.1-8B-Instruct, we developed _Llama-Primus-Base_, _Llama-Primus-Merged_, and _Llama-Primus-Reasoning_. This model card is **Llama-Primus-Reasoning**.
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> **Note:** No TrendMicro customer information is included.
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## Cybersecurity Benchmark Results
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| Model | CISSP | Avg. Tokens |
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|----------------------------------------|----------------------|-------------|
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| **w/o CoT, 5-shot** | | |
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| Llama-3.1-8B-Instruct | 0.7073 | 1 |
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| Llama-Primus-Merged | 0.7191 ↑1.67% | 1 |
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| **w/ CoT, 0-shot** | | |
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| Llama-3.1-8B-Instruct | 0.7288 ↑3.03% | 279.69 |
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| └─ + *Distilled from o1-preview* | 0.7583 ↑7.21% | 646.94 |
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| └─ + *Distilled from DeepSeek-R1* | 0.7859 ↑11.1% | 1667.56 |
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| └─ + *Distilled from (o1 + R1)* | 0.7780 ↑10.0% | 1615.54 |
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| Llama-Primus-Merged | 0.7603 ↑7.49% | 241.92 |
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| └─ + *Distilled from o1-preview* | 0.7780 ↑10.0% | 726.96 |
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| └─ + *Distilled from DeepSeek-R1* | 0.8075 ↑14.2% | 1483.94 |
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| └─ + *Distilled from (o1 + R1)* | 0.8193 ↑**15.8%** | 1467.40 |
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| **Raw Models for Comparison** | | |
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| o1-preview | 0.8035 | 1054.91 |
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| DeepSeek-R1 | 0.8212 | 1229.32 |
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| DeepSeek-R1-Distill-Llama-8B | 0.7399 ↑4.61% | 1542.10 |
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Effect of _Primus-Reasoning_ fine-tuning, evaluated on CISSP. ↑ indicates the percentage improvement over Llama without CoT and in the 5-shot setting. The best improvement is highlighted in **bold**.
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## About _Primus_
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Primus is Trend Micro's pioneering family of lightweight, state-of-the-art open cybersecurity language models and datasets. Developed through our cutting-edge research initiatives and advanced technology, these resources share the innovative foundation that powers our enterprise-class [Trend Cybertron](https://newsroom.trendmicro.com/2025-02-25-Trend-Micro-Puts-Industry-Ahead-of-Cyberattacks-with-Industrys-First-Proactive-Cybersecurity-AI) solution. As an industry leader in cybersecurity, Trend Micro is proud to contribute these powerful, efficiency-optimized models and datasets to the community, while maintaining the excellence and reliability that define our global security standards.
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## License
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This model is based on the MIT license, but you must also comply with the Llama 3.1 Community License Agreement.
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"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|end_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2065
tokenizer_config.json
Normal file
2065
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user