Cmdcaliper Large

🚀 CmdCaliper-large

CmdCaliper模型是由CyCraft AI Lab開發的首個專門為命令行嵌入設計的嵌入模型。評估結果表明，即使是參數約3000萬的CmdCaliper最小版本，在各種特定命令行任務中，也能超越參數多10倍以上（3.35億）的最先進句子嵌入模型。CmdCaliper提供了三種不同大小的模型，為不同硬件資源限制提供了靈活的選擇。

🚀 快速開始

CmdCaliper模型專為命令行嵌入設計，具有出色的性能。你可以通過以下鏈接訪問相關資源：

• [數據集]
• [代碼]
• [論文]

✨ 主要特性

• 針對性設計：首個專門為命令行嵌入設計的嵌入模型。
• 高性能表現：小版本模型在特定命令行任務中能超越參數多10倍以上的先進模型。
• 多種選擇：提供CmdCaliper-large、CmdCaliper-base和CmdCaliper-small三種不同大小的模型，適應不同硬件資源。

📊 評估指標

💻 使用示例

基礎用法

HuggingFace Transformers

import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel

def average_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]

input_texts = [
    'cronjob schedule daily 00:00 ./program.exe',
    'schtasks /create /tn "TaskName" /tr "C:\program.exe" /sc daily /st 00:00',
    'xcopy C:\Program Files (x86) E:\Program Files /E /H /K /O /X'
]

tokenizer = AutoTokenizer.from_pretrained("CyCraftAI/CmdCaliper-base")
model = AutoModel.from_pretrained("CyCraftAI/CmdCaliper-base")

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())

Sentence Transformers

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("CyCraftAI/CmdCaliper-base")
# Run inference
sentences = [
    'cronjob schedule daily 00:00 ./program.exe',
    'schtasks /create /tn "TaskName" /tr "C:\program.exe" /sc daily /st 00:00',
    'xcopy C:\Program Files (x86) E:\Program Files /E /H /K /O /X'
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

⚠️ 侷限性

⚠️ 重要提示

該模型僅專注於Windows命令行。此外，任何長文本將被截斷為最多512個標記。

📄 引用

@inproceedings{huang2024cmdcaliper,
  title={CmdCaliper: A Semantic-Aware Command-Line Embedding Model and Dataset for Security Research},
  author={SianYao Huang, ChengLin Yang, CheYu Lin, and ChunYing Huang},
  booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing,
  year={2024}
}