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--- |
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base_model: llama32-3b |
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library_name: peft |
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pipeline_tag: text-generation |
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tags: |
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- base_model:adapter:llama32-3b |
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- lora |
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- transformers |
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- network-automation |
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- cisco |
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license: llama3.2 |
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language: |
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- es |
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--- |
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# Model Card for Model ID |
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All translatios were done in DeepL.com (free version) |
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EN: |
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This model is an LLM specialized in Cisco network configuration, fine-tuned with 4-bit LoRA on a LLaMA 3.2 3B base, focused on: |
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- Interface configuration |
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- VLAN configuration |
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- DHCP configuration |
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- Technical responses for OSPF, NAT, ACL, DNS, BGP (text mode) |
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In addition, it was designed to integrate with agents that use network automation tools. Developed as a project for the Recent Topics in Networking course at the University of Cauca, it was trained with an artificially generated dataset. |
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The model was trained on a dataset of 10,000 examples, with 10,000 examples of training data and 10,000 examples of test data. |
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ES: |
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Este modelo es un LLM especializado en configuración de redes Cisco, ajustado mediante fine-tuning con LoRA a 4 bits sobre una base LLaMA 3.2 3B, enfocado en: |
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- Configuración de interfaces |
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- Configuración de VLAN |
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- Configuración de DHCP |
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- Respuestas técnicas para OSPF, NAT, ACL, DNS, BGP (modo textual) |
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Además, fue diseñado para integrarse con agentes que usen herramientas para automatización de red. Desarrollado como proyecto para la materia Recent Topics in netwroking |
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de la universidad del cauca, fue entrando con un dataset generado de manera artificial. |
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## Model Details |
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### Model Description |
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EN: |
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This model was adjusted with a specialized dataset of real Cisco commands, with an instruction-input-output structure. |
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It is optimized to run on low-power GPUs thanks to: |
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- 4-bit quantization |
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- LoRA adapters |
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**Key features:** |
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- Natural language understanding in Spanish |
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- Generation of real Cisco commands |
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- Compatible with multi-agent systems |
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- Able to detect when to use external tools |
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ES: |
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Este modelo fue ajustado con un dataset especializado en comandos reales de CiscO, con estructura instrucción–entrada–salida. |
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Está optimizado para ejecutarse en GPUs de bajo consumo gracias a: |
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- Cuantización 4-bit |
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- Adaptadores LoRA |
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- Comprensión de lenguaje natural en español |
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- Generación de comandos Cisco reales |
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- Compatible con sistemas multi-agente |
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- Capaz de detectar cuándo usar herramientas externas |
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**Key features:** |
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- Comprensión de lenguaje natural en español |
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- Generación de comandos Cisco reales |
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- Compatible con sistemas multi-agente |
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- Capaz de detectar cuándo usar herramientas externas |
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**Creditos / Credits** |
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- **Developed by:** Juan Jose Angel Duran Calvache, Alison Daniela Ruiz Muñoz. -Universidad del Cauca |
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- **Funded by [optional]:** Juan Jose Angel Duran Calvache, Alison Daniela Ruiz Muñoz. |
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- **Shared by [optional]:** Juan Jose Angel Duran Calvache, Alison Daniela Ruiz Muñoz. |
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- **Model type:** Causal Language Model (Text Generation) |
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- **Language(s) (NLP):** Español |
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- **License:** LLaMA 3.2 |
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- **Finetuned from model [optional]:** llama32-3b |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/3NombresJJA/Cisco-llm-agent |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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EN: |
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- Direct generation of Cisco IOS configurations |
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- Support for networking students |
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- Simulation of router and switch configurations |
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- Technical conversational assistants |
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ES: |
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- Generación directa de configuraciones Cisco IOS |
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- Soporte a estudiantes de redes |
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- Simulación de configuraciones de routers y switches |
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- Asistentes conversacionales técnicos |
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### Downstream Use [optional] |
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EN: |
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- Integration with LangGraph + LangChain |
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- Automation of real configurations |
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- Virtual laboratory systems |
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- Educational platforms |
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ES: |
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- Integración con LangGraph + LangChain |
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- Automatización de configuraciones reales |
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- Sistemas de laboratorio virtual |
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- Plataformas educativas |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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EN: |
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- Not designed for offensive pentesting |
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- Not designed for production in real critical infrastructures |
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- Does not guarantee security validation |
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ES: |
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- No diseñado para pentesting ofensivo |
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- No diseñado para producción en infraestructuras críticas reales |
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- No garantiza validación de seguridad |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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- The model may invent IP addresses if they are not specified |
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- Does not validate real topologies |
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- May produce incomplete configurations if the prompt is ambiguous |
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- Was only trained on basic to intermediate configurations |
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ES: |
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- El modelo puede inventar direcciones IP si no se especifican |
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- No valida topologías reales |
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- Puede producir configuraciones incompletas si el prompt es ambiguo |
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- Solo fue entrenado en configuraciones básicas – intermedias |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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EN: |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. |
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- Use in educational or simulated environments |
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- Combine with verification agents |
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ES: |
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Los usuarios (tanto directos como secundarios) deben ser conscientes de los riesgos, sesgos y limitaciones del modelo. |
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- Siempre validar las configuraciones antes de aplicarlas a producción |
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- Usar en entornos educativos o simulados |
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- Combinar con agentes de verificación |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```bash |
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pip install transformers peft accelerate bitsandbytes torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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from peft import PeftModel |
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import torch |
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base_model = "llama32-3b" |
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lora_repo = "Awakate/llama32-router-lora" |
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tokenizer = AutoTokenizer.from_pretrained(lora_repo) |
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model = AutoModelForCausalLM.from_pretrained( |
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base_model, |
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load_in_4bit=True, |
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device_map="auto" |
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) |
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model = PeftModel.from_pretrained(model, lora_repo) |
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pipe = pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer, |
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max_new_tokens=200, |
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temperature=0.1 |
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) |
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prompt = "Configura la interfaz Gi0/0 con ip 192.168.1.1 máscara 255.255.255.0 y vlan 10" |
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print(pipe(prompt)[0]["generated_text"]) |
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``` |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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The dataset used is posted in the github link, it was a personalized dastaset in format JSON with the following structure: |
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El dataset utilizado se encuentra publicado en el enlace de GitHub. Se trata de un dataset personalizado en formato JSON con la siguiente estructura: |
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{ |
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"instruction": "Configurar interfaz", |
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"input": "Gi0/0 con IP 192.168.1.1", |
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"output": "interface Gi0/0..." |
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} |
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Contain examples of: Interfaces, VLAN, DHCP, OSPF, NAT, ACL and DNS |
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Contiene ejemplos de: Interfaces, VLAN, DHCP, OSPF, NAT, ACL y DNS |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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- Fine-tuning con LoRA (Low Rank Adaptation) |
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- Cuantización 4-bit |
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- Framework: transformers + peft |
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#### Preprocessing [optional] |
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There is no pre proccesing of the data. |
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No se hizo procesamiento de los datos. |
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#### Training Hyperparameters |
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- **Training regime:** |
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Epochs: 5 |
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Batch size: 2 |
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Gradient accumulation: 8 |
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Learning rate: 8e-5 |
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LoRA r: 16 |
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LoRA alpha: 32 |
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Precision: FP16 |
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LoRA: Dropout: 0.05 |
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Max length: 200 |
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<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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EN: |
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The finetune weights with Lora have a size of 18MB and were processed in an hour and a half of compilation in 115 checkpoints. |
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ES: |
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Los pesos del finetune con lora tienen un peso de 18MB, fue procesado en hora y media de compilación en 115 checkpoints. |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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EN: |
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-Human testing with real prompts |
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-Integration with LangGraph agents |
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-Manual validation of Cisco commands |
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ES: |
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-Pruebas humanas con prompts reales |
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-Integración con agentes LangGraph |
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-Validación manual de comandos Cisco |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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#### Summary |
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EN: |
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The agent works in a curious way. An example of agent integration can be found in the GitHub repository, where it was possible to verify through prompt engineering |
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the understanding of the knowledge model added by the fine-tune. However, it does not respond effectively 100% of the time, so the results must be taken with a grain of salt. |
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The agent works in a curious way. An example of agent integration can be found in the GitHub repository, where it was possible to verify through prompt engineering |
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ES: |
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El agente funciona de forma curiosa, se encuentra un ejemplo de integracion a agente en el repositorio de github, donde se pudo comprobar atravez de prompt engineering |
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el entendimiento del modelo del conocimiento agregado por el finetune, sin embargo no responde de forma efectiva el 100% de las veces por lo que se debe de tomar con detalle |
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los resultados. |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** GPU RTX 4050 6GB laptop version |
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- **Hours used:** 4 |
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- **Cloud Provider:** Local |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** Menos de 0.5 |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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-Base: LLaMA 3.2 – 3B parameters |
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-Adaptation: LoRA |
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-Precision: 4-bit |
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-Objective: Causal Language Modeling |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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Linux in WSL |
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Intel i5 12500H |
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8GB RAM DDR4 |
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RTX 4050 6GB laptop |
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SSD M.2 |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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Proyecto Academico para RTN 2025-2 |
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## Model Card Authors [optional] |
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Juan Jose Angel Duran Calvache |
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Alison Daniela Ruiz Muñoz |
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## Model Card Contact |
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joseduran@unicauca.edu.co |
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alisonruiz@unicauca.edu.co |
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### Framework versions |
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- PEFT 0.18.0 |
