Base Model: Dolphin 2.6 Mistral 7B
Training Data: General Knowledge dataset (37,635 examples)
Training Method: LoRA fine-tuning with Unsloth
Use Case: Cybersecurity education, penetration testing methodology, security research

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("YOUR_USERNAME/dolphin-cybersec")
tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/dolphin-cybersec")

prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
What is SQL injection?

### Input:


### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

Training Details

LoRA Rank: 16
Learning Rate: 2e-4
Training Steps: 1000
Batch Size: 2 (gradient accumulation: 4)

Intended Use

Educational and research purposes only. For learning about cybersecurity concepts and methodologies.

frEEtom3/dolphin-cybersec

Dolphin Cybersecurity Research Model

Model Details

Usage

Training Details

Intended Use

frEEtom3/
dolphin-cybersec