初始化项目，由ModelHub XC社区提供模型

Model: QCRI/AZERG-T1-Mistral
Source: Original Platform

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Modelfile

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+# ollama modelfile auto-generated by llamafactory
+
+FROM .
+
+TEMPLATE """<s>{{ if .System }}{{ .System }}
+
+{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}[INST] {{ .Content }}[/INST]{{ else if eq .Role "assistant" }} {{ .Content }}</s>{{ end }}{{ end }}"""
+
+PARAMETER stop "</s>"
+PARAMETER num_ctx 4096

README.md

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+---
+license: apache-2.0
+datasets:
+- QCRI/AZERG-Dataset
+language:
+- en
+base_model:
+- mistralai/Mistral-7B-Instruct-v0.3
+tags:
+- STIX standard
+- threat intelligence
+- MITRE ATT&CK
+---
+
+# QCRI/AZERG-T1-Mistral
+This model is a fine-tuned version of `mistralai/Mistral-7B-Instruct-v0.3` specialized for `Task 1: STIX Entity Detection`. It has been trained on the [QCRI/AZERG-Dataset](https://huggingface.co/datasets/QCRI/AZERG-Dataset) to identify CTI-related entities (e.g., malware names, threat actors, vulnerabilities) in unstructured text.
+
+This is a specialist model designed for high performance on entity detection within the [AZERG framework](https://github.com/QCRI/azerg/).
+
+## Intended Use
+Use this model to extract potential STIX entities from a given security text passage.
+
+## Example Prompt:
+
+```
+Instruction:
+You are a helpful threat intelligence analyst. Your task is to extract all STIX entities mentioned in the input. To help you, here is a list of the possible STIX entity types.
+STIX entity types:
+- ATTACK_PATTERN: A type of TTP that describes ways that adversaries attempt to compromise targets. (e.g., T1051, T1548.001, etc.)
+[...]
+
+Answer in the following format: <entities>LIST OF IDENTIFIED ENTITIES SEPARATED BY PIPE |</entities>
+
+Input:
+- Text Passage: [INPUT TEXT]
+
+Response:
+```
+
+## Citation
+If you use this model, please cite our paper:
+
+```
+@article{lekssays2025azerg,
+  title={From Text to Actionable Intelligence: Automating STIX Entity and Relationship Extraction},
+  author={Lekssays, Ahmed and Sencar, Husrev Taha and Yu, Ting},
+  journal={arXiv preprint arXiv:2507.16576},
+  year={2025}
+}
+```

config.json

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+{
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+  "architectures": [
+    "MistralForCausalLM"
+  ],
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+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.0",
+  "use_cache": true,
+  "vocab_size": 32768
+}

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+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.45.0"
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