Instructions to use neilopet/glm4-cybersec-v2-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neilopet/glm4-cybersec-v2-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Olafangensan/GLM-4.7-Flash-heretic")
model = PeftModel.from_pretrained(base_model, "neilopet/glm4-cybersec-v2-lora")

Transformers

How to use neilopet/glm4-cybersec-v2-lora with Transformers:

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

pipe = pipeline("text-generation", model="neilopet/glm4-cybersec-v2-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("neilopet/glm4-cybersec-v2-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use neilopet/glm4-cybersec-v2-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "neilopet/glm4-cybersec-v2-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neilopet/glm4-cybersec-v2-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/neilopet/glm4-cybersec-v2-lora

SGLang

How to use neilopet/glm4-cybersec-v2-lora 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 "neilopet/glm4-cybersec-v2-lora" \
    --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": "neilopet/glm4-cybersec-v2-lora",
		"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 "neilopet/glm4-cybersec-v2-lora" \
        --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": "neilopet/glm4-cybersec-v2-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use neilopet/glm4-cybersec-v2-lora 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 neilopet/glm4-cybersec-v2-lora 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 neilopet/glm4-cybersec-v2-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for neilopet/glm4-cybersec-v2-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="neilopet/glm4-cybersec-v2-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use neilopet/glm4-cybersec-v2-lora with Docker Model Runner:
```
docker model run hf.co/neilopet/glm4-cybersec-v2-lora
```

GLM-4.7-Flash Cybersec v2 LoRA

A LoRA adapter fine-tuned on cybersecurity distillation data for penetration testing and vulnerability analysis with tool-calling capabilities.

Model Details

Base model: Olafangensan/GLM-4.7-Flash-heretic (DeepSeek2 MoE, 30B total / ~3B active)
Fine-tuning method: LoRA (r=8, alpha=8) via Unsloth + TRL SFTTrainer
Training data: ~6,800 cybersecurity Q&A pairs distilled from DeepSeek-R1 + ~300 multi-turn tool-calling examples
Dataset: neilopet/cybersec-distillation-data
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj (including MoE expert layers)
Quantization: Trained in 4-bit QLoRA, exported as fp16 LoRA weights
GGUF: Merged and quantized to Q8_0 for llama-server deployment

Evaluation Results

Metric	Score
Knowledge accuracy (CVE/CWE/tool recall)	90% (18/20)
Tool calling accuracy (run() function calls)	100% (10/10)

Evaluated with a custom harness against llama-server using the optimized system prompt. Knowledge accuracy measures correct recall of CVE details, CWE classifications, and Kali tool usage. Tool calling accuracy measures valid structured run(command=...) function calls.

RAG-augmented performance

With RAG context injection (deterministic CVE lookup + HackTricks vector search), knowledge accuracy improves to near-100% as the model reasons over injected ground truth rather than parametric memory.

Usage

With llama-server (recommended)

Merge the LoRA adapter, quantize to GGUF, and serve with llama-server:

# Merge and quantize (requires unsloth)
python -c "
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained('neilopet/glm4-cybersec-v2-lora')
model.save_pretrained_gguf('merged', tokenizer, quantization_method='q8_0')
"

# Serve
llama-server -m merged/unsloth.Q8_0.gguf --ctx-size 16384 --port 8080

With transformers + PEFT

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Olafangensan/GLM-4.7-Flash-heretic")
model = PeftModel.from_pretrained(base, "neilopet/glm4-cybersec-v2-lora")
tokenizer = AutoTokenizer.from_pretrained("neilopet/glm4-cybersec-v2-lora")

System Prompt

You are an expert cybersecurity operator with access to a Kali Linux terminal.

To execute commands, call: run(command="<full shell command>")
Write the command exactly as you would type it in a terminal.

Rules:
- ALWAYS call run() to execute commands. NEVER fabricate or invent command output.
- If you need to run a command, call run() and wait for the result before continuing.
- Run one command at a time. Read the output, then decide the next step.
- Start with safe, non-destructive commands before aggressive ones.
- If a command fails, read the error and adapt your approach.
- Stay within authorized, legal, and explicitly permitted testing scope.
- Report what you actually observed, not what you expected to see.

Training Infrastructure

Hardware: 1x NVIDIA A40 (48GB) on RunPod
Framework: Unsloth + TRL SFTTrainer
Training regime: bf16 mixed precision, QLoRA 4-bit
PEFT version: 0.18.1

Framework versions

PEFT 0.18.1

Downloads last month: 2

Model tree for neilopet/glm4-cybersec-v2-lora

Base model

Olafangensan/GLM-4.7-Flash-heretic

Adapter

(3)

this model