qwen3.6-27b-cybersecurity-lora

📋 Model Details

🎯 Intended Use

This LoRA adapter enhances Qwen3.6-27B's capability in cybersecurity domains including:

• Penetration testing methodology & tools
• Vulnerability assessment & analysis
• Security operations & incident response
• Network security & protocol analysis
• Web application security (OWASP Top 10)
• Malware analysis & reverse engineering
• Cryptography & secure communications
• Security policy & compliance frameworks

⚙️ Training Configuration

Quantization (4-bit NF4)

python
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BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
)

LoRA Configuration

python
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LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=[
        "q_proj", "k_proj", "v_proj", "o_proj",
        "gate_proj", "up_proj", "down_proj",
    ],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

Training Hyperparameters

python
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SFTConfig(
    max_length=512,
    per_device_train_batch_size=1,
    gradient_accumulation_steps=8,
    num_train_epochs=1,
    learning_rate=2e-4,
    bf16=True,
    optim="paged_adamw_8bit",
    warmup_steps=10,
    lr_scheduler_type="cosine",
    logging_steps=10,
)

Training Metrics

Loss converged rapidly within the first ~200 steps, indicating successful knowledge injection into the LoRA adapters.

📦 How to Use

1. Install Dependencies

bash
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pip install torch transformers accelerate peft bitsandbytes

2. Load Model with LoRA Adapter

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

# Base model
model_name = "Qwen/Qwen3.6-27B"

# 4-bit quantization config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
)

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)

# Load LoRA adapter
model = PeftModel.from_pretrained(model, "hotdogs/qwen3.6-27b-cybersecurity-lora")

3. Inference

python
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def generate_response(system_prompt, user_prompt):
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt},
    ]
    text = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    inputs = tokenizer(text, return_tensors="pt").to(model.device)
    
    outputs = model.generate(
        **inputs,
        max_new_tokens=1024,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
response = generate_response(
    "You are a cybersecurity expert. Provide detailed, accurate technical information.",
    "Explain how to identify SQL injection vulnerabilities in a web application."
)
print(response)

4. Merge with Base Model (Optional)

bash
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python -m peft merge_and_save \
    --model_name Qwen/Qwen3.6-27B \
    --peft_model hotdogs/qwen3.6-27b-cybersecurity-lora \
    --output_dir ./qwen3.6-27b-cybersecurity-merged

📊 Training Details

Dataset

• Source: AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.1
• Format: Chat template (system/user/assistant)
• Sample size: 5,000 rows
• Topics: Penetration testing, vulnerability assessment, network security, web security, cryptography, malware analysis, forensics, security policies

Training Environment

Memory Usage

🔗 References

• Qwen3.6-27B Model
• QLoRA: Efficient Finetuning of Quantized Language Models
• LoRA: Low-Rank Adaptation of Large Language Models
• Cybersecurity-Dataset-Fenrir-v2.1
• TRL (Transformer Reinforcement Learning)

⚠️ Limitations

• Trained on a subset (5K/99K) of the full dataset — may not cover all cybersecurity topics exhaustively
• LoRA rank 8 — limited representation capacity compared to full fine-tune
• 4-bit quantization introduces minor precision loss vs half-precision
• Domain knowledge is limited to the training dataset coverage

📝 Notes

• This adapter was trained using CPU offload-free 4-bit NF4 quantization on a single RTX 4090
• Gradient checkpointing was used to reduce VRAM consumption during training
• The prepare_model_for_kbit_training step was skipped to avoid OOM from fp32 casting of large tensors — gradients are computed directly in bf16

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Created by: hotdogs
Training Date: June 17, 2026

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💖 Support / สนับสนุน

If you find this model useful, please consider supporting my work!
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