Instructions to use NiffyHunt90/wraithcore-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NiffyHunt90/wraithcore-7b with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "NiffyHunt90/wraithcore-7b")

Transformers

How to use NiffyHunt90/wraithcore-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NiffyHunt90/wraithcore-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("NiffyHunt90/wraithcore-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NiffyHunt90/wraithcore-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NiffyHunt90/wraithcore-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NiffyHunt90/wraithcore-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NiffyHunt90/wraithcore-7b

SGLang

How to use NiffyHunt90/wraithcore-7b 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 "NiffyHunt90/wraithcore-7b" \
    --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": "NiffyHunt90/wraithcore-7b",
		"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 "NiffyHunt90/wraithcore-7b" \
        --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": "NiffyHunt90/wraithcore-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use NiffyHunt90/wraithcore-7b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for NiffyHunt90/wraithcore-7b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for NiffyHunt90/wraithcore-7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NiffyHunt90/wraithcore-7b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="NiffyHunt90/wraithcore-7b",
    max_seq_length=2048,
)

Docker Model Runner
How to use NiffyHunt90/wraithcore-7b with Docker Model Runner:
```
docker model run hf.co/NiffyHunt90/wraithcore-7b
```

WraithCore 7B

Security operations LoRA adapter fine-tuned on Qwen 2.5 7B. This is the lightweight adapter variant of WraithWall Core V3 — same training data, smaller footprint (616MB adapter vs 15GB merged).

What it knows

Trained on 7,000 structured Q&A pairs from 8 live intelligence feeds:

CISA KEV — 1,991 actively exploited vulnerabilities
MITRE ATT&CK — 1,103 technique mappings with detection rules
AbuseIPDB — 839 real attacker IPs with abuse attribution
Malware Intelligence — 716 entries (URLhaus, SSL Blacklist, MalwareBazaar)
Infrastructure Defense — 506 entries on SSH hardening, container security, honeypots
Cowrie Honeypot — 370 entries from live SSH/Telnet attack sessions
Threat Intelligence — 200 entries on campaign correlation, identity graphs
Phishing & BGP — 275 entries on domain analysis, route hijacks

Dataset

7,000 curated security Q&A pairs covering honeypot deployment, BGP monitoring, web app security (OWASP Top 10), LLM prompt injection, malware analysis, incident response, network forensics, and API security. Compiled from real-world production honeypot logs, incident reports, and adversarial testing. No synthetic or GPT-generated data.

Capabilities

Domain	What it does
Vulnerability triage	Classifies CVEs, maps to MITRE, recommends patch priority
Honeypot analysis	Analyzes Cowrie sessions, identifies attacker TTPs
Threat hunting	Correlates IPs and campaigns across sessions
Malware triage	Identifies malware families, extracts IOCs
Phishing detection	Analyzes domains for typosquatting
Infrastructure defense	SSH hardening, container isolation, honeypot deployment
BGP intelligence	Route hijack detection and ASN analysis

How to use

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit",
    torch_dtype=torch.float16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, "NiffyHunt90/wraithcore-7b")
tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit")

prompt = "What is the MITRE ATT&CK framework and how do I use it?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training

Base model: Qwen 2.5 7B Instruct (4-bit)
Method: LoRA (r=16, alpha=16)
Adapter size: 616 MB
Hardware: 2x Tesla T4 (14.5GB VRAM)
Framework: Unsloth + HuggingFace TRL
Epochs: 3 | Loss: 3.44 → 0.11 (96.8% reduction)

Related models

WraithWall Core V3 — full merged 16-bit model
CodeGuard Security — code vulnerability detection

Author

Adewale Babalola (Niffyhunt) — Founder, WraithWall

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