---
language:
- en

license: apache-2.0

base_model: HuggingFaceTB/SmolLM2-360M

pipeline_tag: text-generation

tags:
- cybersecurity
- instruction-tuning
- smollm2
- causal-lm
- transformers
- pytorch
- llm
- ai-security

datasets:
- Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset

library_name: transformers
---

# smol-lm2-360-instruct

smol-lm2-360-instruct is an instruction-tuned language model built by fine-tuning **SmolLM2-360M Base** on a cybersecurity instruction dataset. The objective is to improve the model's ability to answer cybersecurity-related questions while following instruction-style prompts.

> **Note:** This is an educational project for learning Instruction Fine-Tuning (IFT) and should not be considered a production-grade security assistant.

---

# Model Details

| Attribute | Value |
|-----------|-------|
| Base Model | HuggingFaceTB/SmolLM2-360M |
| Model Type | Causal Language Model |
| Fine-tuning | Full Parameter Instruction Fine-Tuning |
| Domain | Cybersecurity |
| Framework | Hugging Face Transformers |
| Training Library | Transformers Trainer |
| Language | English |

---

# Training Objective

The model was trained using a supervised instruction-tuning objective.

Each training example follows the structure:

```text
<|system|>
You are a cybersecurity assistant.

<|user|>
Explain SQL Injection.

<|assistant|>
SQL Injection is...
```

During training:

- The **System** prompt provides behavioral context.
- The **User** prompt provides the instruction.
- Loss is computed **only on the Assistant response**, while the System and User tokens are masked from the loss.

This enables the model to learn instruction following while conditioning on the provided context.

---

# Training Configuration

| Parameter | Value |
|-----------|-------|
| Epochs | 4 |
| Learning Rate | 2e-4 |
| Scheduler | Cosine |
| Warmup | 10% |
| Batch Size | 2 |
| Gradient Accumulation | 8 |
| Precision | FP16 |
| Optimizer | AdamW |

---

# Dataset

The model was trained on a cybersecurity instruction dataset consisting of:

- System prompts
- User instructions
- Assistant responses

The dataset covers topics such as:

- Network Security
- Malware Analysis
- Threat Intelligence
- Secure Coding
- Incident Response
- Vulnerability Management
- Security Best Practices
- MITRE ATT&CK
- NIST Cybersecurity Framework

---

# Example

## Prompt

```text
<|system|>
You are a cybersecurity expert.

<|user|>
Explain the difference between symmetric and asymmetric encryption.

<|assistant|>
```

## Output

```text
Symmetric encryption uses a single shared key for both encryption and decryption, making it computationally efficient and suitable for encrypting large amounts of data. Asymmetric encryption uses a public/private key pair, enabling secure key exchange and digital signatures but at a higher computational cost.
```

---

# Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Aravindan/smol-lm2-360-instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = """
<|system|>
You are a cybersecurity assistant.

<|user|>
Explain SQL Injection.

<|assistant|>
"""

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    temperature=0.7
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

# Limitations

This model:

- is intended for educational and research purposes
- may generate incorrect or outdated cybersecurity advice
- has not undergone safety alignment comparable to production LLMs
- should not be used as the sole source for security decisions

Always verify security recommendations with authoritative sources.

---

# Future Improvements

- Larger instruction datasets
- Multi-turn conversation training
- Preference Optimization (DPO/ORPO)
- Retrieval-Augmented Generation (RAG)
- LoRA and QLoRA fine-tuning
- Evaluation on cybersecurity benchmarks

---

# Acknowledgements

- Hugging Face
- SmolLM2 Team
- Transformers
- Datasets
- PyTorch

---

# Citation

If you use this model in your work, please cite this repository.