---
language:
- en
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- qwen
- cisco
- ccna
- networking
- router
- switch
- ai
- instruct
- sft
pipeline_tag: text-generation
library_name: transformers
datasets:
- Rzkoohi/CCNA_small
---

# Qwen2.5-1.5B-CCNA

A domain-specialized version of **Qwen2.5-1.5B-Instruct**, fine-tuned for **Cisco CCNA** concepts and networking tasks.

The model is designed to provide more accurate responses to CCNA-related questions than the base model while remaining lightweight enough to run on modest hardware.

---

# Model Details

| Property | Value |
|----------|-------|
| Model Name | Qwen2.5-1.5B-CCNA |
| Base Model | Qwen/Qwen2.5-1.5B-Instruct |
| Fine-tuning Method | Supervised Fine-Tuning (SFT) |
| Domain | Computer Networking |
| Specialization | Cisco CCNA |
| Language | English |

---

# Dataset

This model was fine-tuned using:

**Dataset:** `Rzkoohi/CCNA_small`

The dataset contains instruction-answer pairs focused on Cisco CCNA topics including:

- IPv4
- IPv6
- Subnetting
- VLANs
- Trunking
- Inter-VLAN Routing
- STP
- EtherChannel
- Static Routing
- OSPF
- ACLs
- NAT/PAT
- DHCP
- DNS
- Cisco IOS CLI
- Network Troubleshooting
- General CCNA Theory

---

# Intended Use

This model is intended for:

- Cisco networking education
- Cisco CLI assistance
- Network troubleshooting
- AI networking assistants
- Educational chatbots
- Local LLM deployments

---

# Limitations

This model is specialized for Cisco CCNA topics.

While it performs well on networking questions, it may not perform as well on unrelated subjects.

Configuration examples should always be reviewed before deployment in production environments.

---

# Training

- **Base Model:** Qwen/Qwen2.5-1.5B-Instruct
- **Dataset:** Rzkoohi/CCNA_small
- **Training Type:** Supervised Fine-Tuning (SFT)

---

# Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Rzkoohi/Qwen2.5-4B-CCNA"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto"
)

messages = [
    {
        "role": "user",
        "content": "Explain OSPF Areas."
    }
]

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=512
)

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

---

# License

This model inherits the license of the base model:

**Qwen/Qwen2.5-1.5B-Instruct**

Please refer to the original model license for complete terms of use.

---

# Citation

If you use this model in research or projects, please cite:

```bibtex
@misc{koohi2026ccna,
  author = {Reza Koohi},
  title = {Qwen2.5-1.5B-CCNA},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/Rzkoohi/Qwen2.5-4B-CCNA}
}
```

---

# Acknowledgements

This project is built upon the excellent **Qwen2.5** foundation model developed by the Qwen Team.

Special thanks to the open-source AI community and Hugging Face for providing the ecosystem that makes projects like this possible.

---

## ⭐ If this model helps you, please consider giving it a Like on Hugging Face.