Update README.md

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README.md

@@ -6,35 +6,90 @@ tags:
 - base_model:adapter:llama32-3b
 - lora
 - transformers
+- network-automation
+- cisco
+license: llama3.2
+language:
+- es
 ---
 
 # Model Card for Model ID
+All translatios were done in  DeepL.com (free version)
+EN: 
+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:
 
-<!-- Provide a quick summary of what the model is/does. -->
+- Interface configuration
+- VLAN configuration
+- DHCP configuration
+- Technical responses for OSPF, NAT, ACL, DNS, BGP (text mode)
 
+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.
+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. 
+
+ES:
+
+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:
+
+- Configuración de interfaces
+- Configuración de VLAN
+- Configuración de DHCP
+- Respuestas técnicas para OSPF, NAT, ACL, DNS, BGP (modo textual)
+
+Además, fue diseñado para integrarse con agentes que usen herramientas para automatización de red. Desarrollado como proyecto para la materia Recent Topics in netwroking
+de la universidad del cauca, fue entrando con un dataset generado de manera artificial. 
 
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+EN:
+This model was adjusted with a specialized dataset of real Cisco commands, with an instruction-input-output structure.  
+It is optimized to run on low-power GPUs thanks to:
+
+- 4-bit quantization
+- LoRA adapters
+
+**Key features:**
+
+- Natural language understanding in Spanish
+- Generation of real Cisco commands
+- Compatible with multi-agent systems
+- Able to detect when to use external tools
+
+ES:
+Este modelo fue ajustado con un dataset especializado en comandos reales de CiscO, con estructura instrucción–entrada–salida.  
+Está optimizado para ejecutarse en GPUs de bajo consumo gracias a:
 
+- Cuantización 4-bit
+- Adaptadores LoRA
 
+- Comprensión de lenguaje natural en español
+- Generación de comandos Cisco reales
+- Compatible con sistemas multi-agente
+- Capaz de detectar cuándo usar herramientas externas
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+**Key features:**
+
+- Comprensión de lenguaje natural en español
+- Generación de comandos Cisco reales
+- Compatible con sistemas multi-agente
+- Capaz de detectar cuándo usar herramientas externas
+
+
+- **Developed by:** Juan Jose Angel Duran Calvache, Alison Daniela Ruiz Muñoz. -Universidad del Cauca
+- **Funded by [optional]:** Juan Jose Angel Duran Calvache, Alison Daniela Ruiz Muñoz.
+- **Shared by [optional]:** Juan Jose Angel Duran Calvache, Alison Daniela Ruiz Muñoz.
+- **Model type:** Causal Language Model (Text Generation)
+- **Language(s) (NLP):** Español
+- **License:** LLaMA 3.2
+- **Finetuned from model [optional]:** llama32-3b
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
+- **Repository:** https://github.com/3NombresJJA/Cisco-llm-agent
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
@@ -43,40 +98,116 @@ tags:
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 ### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+EN:
+- Direct generation of Cisco IOS configurations
+- Support for networking students
+- Simulation of router and switch configurations
+- Technical conversational assistants
+
+ES:
+- Generación directa de configuraciones Cisco IOS
+- Soporte a estudiantes de redes
+- Simulación de configuraciones de routers y switches
+- Asistentes conversacionales técnicos
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+EN:
+- Integration with LangGraph + LangChain
+- Automation of real configurations
+- Virtual laboratory systems
+- Educational platforms
 
-[More Information Needed]
+ES:
+- Integración con LangGraph + LangChain
+- Automatización de configuraciones reales
+- Sistemas de laboratorio virtual
+- Plataformas educativas
 
 ### Out-of-Scope Use
 
+
+
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+EN:
+- Not designed for offensive pentesting
+- Not designed for production in real critical infrastructures
+- Does not guarantee security validation
+
+ES:
+- No diseñado para pentesting ofensivo
+- No diseñado para producción en infraestructuras críticas reales
+- No garantiza validación de seguridad
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
-
+EN:
+- The model may invent IP addresses if they are not specified
+- Does not validate real topologies
+- May produce incomplete configurations if the prompt is ambiguous
+- Was only trained on basic to intermediate configurations
+
+ES:
+- El modelo puede inventar direcciones IP si no se especifican
+- No valida topologías reales
+- Puede producir configuraciones incompletas si el prompt es ambiguo
+- Solo fue entrenado en configuraciones básicas – intermedias
 ### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+EN:
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
+- Use in educational or simulated environments
+- Combine with verification agents
+
+ES:
+Los usuarios (tanto directos como secundarios) deben ser conscientes de los riesgos, sesgos y limitaciones del modelo.
+- Siempre validar las configuraciones antes de aplicarlas a producción
+- Usar en entornos educativos o simulados
+- Combinar con agentes de verificación
+
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```bash
+pip install transformers peft accelerate bitsandbytes torch
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from peft import PeftModel
+import torch
+
+base_model = "llama32-3b"
+lora_repo = "Awakate/llama32-router-lora"
+
+tokenizer = AutoTokenizer.from_pretrained(lora_repo)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    load_in_4bit=True,
+    device_map="auto"
+)
+
+model = PeftModel.from_pretrained(model, lora_repo)
+
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=200,
+    temperature=0.1
+)
+
+prompt = "Configura la interfaz Gi0/0 con ip 192.168.1.1 máscara 255.255.255.0 y vlan 10"
+print(pipe(prompt)[0]["generated_text"])
+
+
+```
+
 
 ## Training Details
 
@@ -84,26 +215,58 @@ Use the code below to get started with the model.
 
 <!-- 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. -->
 
-[More Information Needed]
+The dataset used is posted in the github link, it was a personalized dastaset in format JSON with the following structure:
+
+El dataset utilizado se encuentra publicado en el enlace de GitHub. Se trata de un dataset personalizado en formato JSON con la siguiente estructura:
+{
+  "instruction": "Configurar interfaz",
+  "input": "Gi0/0 con IP 192.168.1.1",
+  "output": "interface Gi0/0..."
+}
+Contain examples of:  Interfaces, VLAN, DHCP, OSPF, NAT, ACL and DNS
+Contiene ejemplos de: Interfaces, VLAN, DHCP, OSPF, NAT, ACL y DNS
 
 ### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
+- Fine-tuning con LoRA (Low Rank Adaptation)
+- Cuantización 4-bit
+- Framework: transformers + peft
+
+
 #### Preprocessing [optional]
 
-[More Information Needed]
 
 
+There is no pre proccesing of the data.
+No se hizo procesamiento de los datos.
+
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Training regime:**
+Epochs: 5
+Batch size: 2
+Gradient accumulation: 8
+Learning rate: 8e-5
+LoRA r: 16
+LoRA alpha: 32
+Precision: FP16
+LoRA: Dropout: 0.05
+Max length: 200
+
+<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
 #### Speeds, Sizes, Times [optional]
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
+EN:
+The finetune weights with Lora have a size of 18MB and were processed in an hour and a half of compilation in 115 checkpoints.
+
+ES:
+Los pesos del finetune con lora tienen un peso de 18MB, fue procesado en hora y media de compilación en 115 checkpoints.
+
 
 ## Evaluation
 
@@ -115,7 +278,15 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card if possible. -->
 
-[More Information Needed]
+EN:
+-Human testing with real prompts
+-Integration with LangGraph agents
+-Manual validation of Cisco commands
+
+ES:
+-Pruebas humanas con prompts reales
+-Integración con agentes LangGraph
+-Validación manual de comandos Cisco
 
 #### Factors
 
@@ -131,11 +302,27 @@ Use the code below to get started with the model.
 
 ### Results
 
-[More Information Needed]
+
+![Screenshot 2025-12-04 145318](https://cdn-uploads.huggingface.co/production/uploads/6922899fcc2ce4db552cdafd/QmdvNwWzCS7PMjX6-nxqQ.png)
+
+
+![Screenshot 2025-12-04 115148](https://cdn-uploads.huggingface.co/production/uploads/6922899fcc2ce4db552cdafd/FdROKu4Sg3gkp2qi3cbca.png)
+
+
+![Screenshot 2025-12-04 145542](https://cdn-uploads.huggingface.co/production/uploads/6922899fcc2ce4db552cdafd/yysGWm2uYM5C6NRyQRuyD.png)
+
 
 #### Summary
 
+EN:
+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
+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.
+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
 
+ES:
+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
+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
+los resultados.
 
 ## Model Examination [optional]
 
@@ -149,17 +336,20 @@ Use the code below to get started with the model.
 
 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).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
+- **Hardware Type:** GPU RTX 4050 6GB laptop version
+- **Hours used:** 4
+- **Cloud Provider:** Local
 - **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- **Carbon Emitted:** Menos de 0.5
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+-Base: LLaMA 3.2 – 3B parameters
+-Adaptation: LoRA
+-Precision: 4-bit
+-Objective: Causal Language Modeling
 
 ### Compute Infrastructure
 
@@ -167,7 +357,12 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 #### Hardware
 
-[More Information Needed]
+Linux in WSL
+
+Intel i5 12500H
+8GB RAM DDR4
+RTX 4050 6GB laptop
+SSD M.2
 
 #### Software
 
@@ -193,15 +388,18 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## More Information [optional]
 
-[More Information Needed]
+Proyecto Academico para RTN 2025-2
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+Juan Jose Angel Duran Calvache
+Alison Daniela Ruiz Muñoz
 
 ## Model Card Contact
 
-[More Information Needed]
+joseduran@unicauca.edu.co
+alisonruiz@unicauca.edu.co
+
 ### Framework versions
 
-- PEFT 0.18.0
+- PEFT 0.18.0