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Model: Aravindan/smol-lm2-360-instruct
Source: Original Platform

README.md

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+---
+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.