Text Generation
PEFT
Safetensors
Transformers
cybersecurity
soc
security-operations-center
microsoft-sentinel
azure-sentinel
siem
incident-response
threat-hunting
kql
lora
conversational
Instructions to use pulkitrai/SOC7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use pulkitrai/SOC7 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B") model = PeftModel.from_pretrained(base_model, "pulkitrai/SOC7") - Transformers
How to use pulkitrai/SOC7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="pulkitrai/SOC7") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pulkitrai/SOC7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use pulkitrai/SOC7 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pulkitrai/SOC7" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pulkitrai/SOC7", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/pulkitrai/SOC7
- SGLang
How to use pulkitrai/SOC7 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "pulkitrai/SOC7" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pulkitrai/SOC7", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "pulkitrai/SOC7" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pulkitrai/SOC7", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use pulkitrai/SOC7 with Docker Model Runner:
docker model run hf.co/pulkitrai/SOC7
|
Download README.md from pulkitrai/SOC7: direct link, hf CLI and curl.
- Browser
- Download file 2.12 kB
-
https://huggingface.co/pulkitrai/SOC7/resolve/main/README.md
- Command line
-
hf download hf://pulkitrai/SOC7/README.md
-
curl -L -o README.md https://huggingface.co/pulkitrai/SOC7/resolve/main/README.md
2.12 kB
| base_model: Qwen/Qwen3-0.6B | |
| library_name: peft | |
| pipeline_tag: text-generation | |
| tags: | |
| - cybersecurity | |
| - soc | |
| - security-operations-center | |
| - microsoft-sentinel | |
| - azure-sentinel | |
| - siem | |
| - incident-response | |
| - threat-hunting | |
| - kql | |
| - lora | |
| - peft | |
| - transformers | |
| - conversational | |
| license: apache-2.0 | |
| # SOC7 — Natural Language SOC Assistant | |
| SOC7 is a cybersecurity-focused conversational AI assistant designed to help Security Operations Center (SOC) analysts interact with Microsoft Sentinel using natural-language prompts. | |
| Instead of requiring an analyst to manually construct every Kusto Query Language (KQL) query, SOC7 is designed to understand questions written in plain English and help translate them into Sentinel-oriented investigations. | |
| For example: | |
| > "Show me failed login attempts for the last 24 hours." | |
| SOC7 can help generate the appropriate Sentinel/KQL investigation query and explain what the query is looking for. | |
| > **Important:** SOC7 is a language-model adapter. It does not automatically connect to or access your Microsoft Sentinel environment by itself. A separate application, API integration, or agent workflow is required to execute generated queries against Sentinel. | |
| --- | |
| ## Model Details | |
| ### Model Description | |
| SOC7 is a lightweight LoRA adapter fine-tuned from `Qwen/Qwen3-0.6B` for cybersecurity and SOC-oriented conversational tasks. | |
| The model is intended to assist analysts with: | |
| - Natural-language to KQL translation | |
| - Microsoft Sentinel investigation queries | |
| - Security log analysis | |
| - Alert investigation | |
| - Threat-hunting questions | |
| - Authentication and sign-in investigations | |
| - IP and user investigation | |
| - Basic incident-response assistance | |
| - Explaining KQL queries in simple language | |
| - Identifying useful Sentinel tables for an investigation | |
| - Structuring SOC investigation workflows | |
| ### Example | |
| **Analyst:** | |
| > Find users with more than 5 failed sign-ins in the last hour. | |
| **SOC7:** | |
| ```kql | |
| SigninLogs | |
| | where TimeGenerated >= ago(1h) | |
| | where ResultType != "0" | |
| | summarize FailedAttempts=count() by UserPrincipalName | |
| | where FailedAttempts > 5 | |
| | order by FailedAttempts desc |