Instructions to use Awakate/Cisco-Configuration-agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Awakate/Cisco-Configuration-agent with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("./models/llama32-3b")
model = PeftModel.from_pretrained(base_model, "Awakate/Cisco-Configuration-agent")

Transformers

How to use Awakate/Cisco-Configuration-agent with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Awakate/Cisco-Configuration-agent")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Awakate/Cisco-Configuration-agent", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Awakate/Cisco-Configuration-agent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Awakate/Cisco-Configuration-agent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Awakate/Cisco-Configuration-agent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Awakate/Cisco-Configuration-agent

SGLang

How to use Awakate/Cisco-Configuration-agent with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Awakate/Cisco-Configuration-agent" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Awakate/Cisco-Configuration-agent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Awakate/Cisco-Configuration-agent" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Awakate/Cisco-Configuration-agent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Awakate/Cisco-Configuration-agent with Docker Model Runner:
```
docker model run hf.co/Awakate/Cisco-Configuration-agent
```

Awakate commited on Dec 7, 2025

Commit

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@@ -15,7 +15,9 @@ language:
 # 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:
 - Interface configuration
@@ -44,6 +46,7 @@ de la universidad del cauca, fue entrando con un dataset generado de manera arti
 ### Model Description
 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:
@@ -77,6 +80,7 @@ Está optimizado para ejecutarse en GPUs de bajo consumo gracias a:
 - 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 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:
 - Interface configuration
 ### Model Description
 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:
 - 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.