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Model: Rzkoohi/Qwen2.5-4B-CCNA
Source: Original Platform

README.md

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+---
+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.