Instructions to use hotdogs/Agents-A1-4B-cyber-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hotdogs/Agents-A1-4B-cyber-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/root/models/Agents-A1-4B")
model = PeftModel.from_pretrained(base_model, "hotdogs/Agents-A1-4B-cyber-lora")

Transformers

How to use hotdogs/Agents-A1-4B-cyber-lora with Transformers:

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

pipe = pipeline("text-generation", model="hotdogs/Agents-A1-4B-cyber-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hotdogs/Agents-A1-4B-cyber-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hotdogs/Agents-A1-4B-cyber-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hotdogs/Agents-A1-4B-cyber-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": "hotdogs/Agents-A1-4B-cyber-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hotdogs/Agents-A1-4B-cyber-lora

SGLang

How to use hotdogs/Agents-A1-4B-cyber-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 "hotdogs/Agents-A1-4B-cyber-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": "hotdogs/Agents-A1-4B-cyber-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 "hotdogs/Agents-A1-4B-cyber-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": "hotdogs/Agents-A1-4B-cyber-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hotdogs/Agents-A1-4B-cyber-lora with Docker Model Runner:
```
docker model run hf.co/hotdogs/Agents-A1-4B-cyber-lora
```

🛡️ Agents-A1-4B-Cyber-LoRA

Cybersecurity LoRA Adapter for Agents-A1-4B — Offensive & Defensive Security Expert

Cybersecurity LoRA adapter fine-tuned from InternScience/Agents-A1-4B on 168K+ curated cybersecurity samples covering offensive security, defensive strategies, threat intelligence, incident response, and vulnerability analysis. Includes GGUF LoRA for llama.cpp inference with --lora-scaled.

✨ Key Features

Capability	Description
🛡️ Cyber Expert	Offensive + defensive security, penetration testing, red/blue team
🧠 Causal Analysis	Step-by-step security reasoning (MITRE ATT&CK, OWASP)
🚨 Incident Response	Detection, containment, and response playbooks
🔍 Threat Intel	CVE analysis, IOC correlation, ransomware indicators
💻 Code & Exploit	Python/Bash security tooling, exploit analysis
🎛️ Adjustable	`--lora-scaled` lets you control LoRA strength (0.1–0.7+)
🌏 Thai + English	Native bilingual base model support

🧬 Model Information

Parameter	Value
Base Model	InternScience/Agents-A1-4B
Parameters	~4.62B (4.29B trainable-excluded)
Architecture	Qwen3.5 hybrid (Linear + Full attention)
LoRA Rank	r=64, alpha=128, dropout=0.0
Target Modules	q/k/v/o_proj, gate/up/down_proj (all linear)
Training Data	168,793 samples (Fenrir + Trendyol + ShareGPT)
Max Seq Length	4096 tokens
Training Steps	40,089 (1 epoch)
Format	ChatML (Jinja2 template)

📊 Training Data

Trained on 3 curated cybersecurity datasets (all English, unified system prompt):

Dataset	Rows	Content
AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.1	99,869	Causal analysis, threat intel, MITRE, ransomware, red/blue team
Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset	53,201	Cyber-defense, C2 analysis, DLP, honeypot, insider threat
ChaoticNeutrals/Cybersecurity-ShareGPT	15,723	Cybersecurity Q&A + step-by-step reasoning

Training details:

Method: BF16 LoRA via Unsloth (no quantization loss)
Optimizer: AdamW 8-bit, lr=1e-4, cosine schedule, warmup 3%
Effective batch size: 16 (4× RTX 3060 12GB)

🚀 Usage

llama.cpp — LoRA (Recommended)

This repo ships gguf/cyber_lora_v1.gguf — a GGUF-format LoRA adapter that works with any Agents-A1-4B base GGUF via --lora-scaled.

Tune the strength with the scale value:

0.1 — subtle cyber knowledge boost (keeps base personality)
0.3 — balanced security-aware assistant
0.5 — strong cybersecurity expert behavior
0.7 — heavy security specialization (may override base style)
>0.7 — possible overfit artifacts / style override; test per use case

# With our Fable heretic GGUF (recommended — uncensored + cyber)
llama-server \
  -m /models/Agents-A1-4B-Fable-Preview-heretic-F16.gguf \
  --lora-scaled /models/cyber_lora_v1.gguf:0.5 \
  --host 0.0.0.0 --port 8080 \
  --n-gpu-layers 999 \
  --ctx-size 32768 \
  --flash-attn on \
  --cont-batching \
  --mlock \
  --temp 0.95 \
  --top-k 40 \
  --top-p 0.9 \
  --min-p 0.0 \
  -n -1 \
  --no-mmap \
  --parallel 1 --tools all \
  --dry-multiplier 0.0 \
  --jinja --dry-sequence-breaker none --repeat-penalty 1.1 

# Or with our Kimi heretic GGUF (coding-focused base)
llama-server \
  -m /models/Agents-A1-4B-kimi-Preview-heretic-IQ4_NL.gguf \
  --lora-scaled /models/cyber_lora_v1.gguf:0.5 \
  --host 0.0.0.0 --port 8080 \
  --n-gpu-layers 999 \
  --ctx-size 32768 \
  --flash-attn on \
  --cont-batching \
  --mlock \
  --temp 0.95 \
  --top-k 40 \
  --top-p 0.9 \
  --min-p 0.0 \
  -n -1 \
  --no-mmap \
  --parallel 1 --tools all \
  --dry-multiplier 0.0 \
  --jinja --dry-sequence-breaker none --repeat-penalty 1.1 

# CLI quick test
llama-cli -m Agents-A1-4B-Fable-Preview-heretic-F16.gguf \
  --lora-scaled cyber_lora_v1.gguf:0.5 \
  -p "What is SQL injection and how do I prevent it?" -n 256

💡 Compatible base models (any Agents-A1-4B GGUF works):

hotdogs/Agents-A1-4B-Fable-Preview-heretic-GGUF

hotdogs/Agents-A1-4B-kimi-Preview-heretic-GGUF

Since LoRA only adds weights, you can also apply it to the plain InternScience/Agents-A1-4B GGUF of your choice.

Python (Transformers + PEFT)

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "InternScience/Agents-A1-4B"
adapter = "hotdogs/Agents-A1-4B-cyber-lora"

model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="auto", device_map="auto", trust_remote_code=True)
model = PeftModel.from_pretrained(model, adapter)
tokenizer = AutoTokenizer.from_pretrained(adapter)

messages = [
    {"role": "system", "content": "You are an elite AI security expert specializing in offensive security, defensive strategies, threat intelligence, incident response, and vulnerability analysis."},
    {"role": "user", "content": "What is SQL injection and how do I prevent it?"},
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.6)
print(tokenizer.decode(outputs[0]))

💬 Example Prompts

Prompt (Thai)	English Equivalent
`SQL injection คืออะไร ป้องกันยังไง`	`What is SQL injection and how do I prevent it?`
`ตรวจจับ lateral movement ด้วย Windows Event ID ยังไง`	`How would you detect lateral movement using Windows Event ID correlation?`
`MITRE ATT&CK ใช้ยังไงใน incident response`	`How is MITRE ATT&CK used in incident response?`
`สัญญาณ ransomware มีอะไรบ้าง SOC ต้องทำยังไง`	`What are common indicators of ransomware and how should a SOC respond?`

# Thai prompt via llama-cli
llama-cli -m Agents-A1-4B-Fable-Preview-heretic-F16.gguf \
  --lora-scaled cyber_lora_v1.gguf:0.5 \
  --flash-attn on --cont-batching --mlock \
  --temp 0.95 --top-k 40 --top-p 0.9 --min-p 0.0 \
  --no-mmap --parallel 1 --tools all \
  --dry-multiplier 0.0 --jinja --dry-sequence-breaker none \
  -p "SQL injection คืออะไร ป้องกันยังไง" -n 256

📦 Downloads

File	Size	Description
`adapter_model.safetensors`	340 MB	PEFT LoRA adapter (BF16)
`gguf/cyber_lora_v1.gguf`	339 MB	GGUF LoRA for llama.cpp `--lora-scaled`
`tokenizer.json`	20 MB	Tokenizer
`chat_template.jinja`	8.98 kB	ChatML Jinja template

⚠️ Disclaimer

This model is fine-tuned on cybersecurity content including offensive techniques. Use it only for authorized security testing, education, and defensive purposes. The authors are not responsible for any misuse. Always follow applicable laws and obtain permission before testing systems you do not own.

🙏 Acknowledgements / ขอบคุณ

InternScience — Agents-A1-4B base model
AlicanKiraz0 — Fenrir v2.1 dataset
Trendyol — Cybersecurity Instruction Tuning dataset
The Chaotic Neutrals — Cybersecurity-ShareGPT dataset
Unsloth AI — Training optimizations
All dataset contributors and the open-source security community ❤️

💖 Support / โปรดสนับสนุน

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

Bitcoin QR — Donate

₿ Bitcoin — BTC:

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Thank you for your support! 🙏✨ ขอบคุณมากๆ สำหรับการสนับสนุนค่า! 💖🤗

Built with ❤️ by UKA — 18-year-old coder & cybersecurity expert

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qwen35

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