Instructions to use Danielbrdz/BarcenasMexico-270m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Danielbrdz/BarcenasMexico-270m with Transformers:

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

pipe = pipeline("text-generation", model="Danielbrdz/BarcenasMexico-270m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Danielbrdz/BarcenasMexico-270m")
model = AutoModelForCausalLM.from_pretrained("Danielbrdz/BarcenasMexico-270m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Danielbrdz/BarcenasMexico-270m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Danielbrdz/BarcenasMexico-270m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Danielbrdz/BarcenasMexico-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Danielbrdz/BarcenasMexico-270m

SGLang

How to use Danielbrdz/BarcenasMexico-270m 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 "Danielbrdz/BarcenasMexico-270m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Danielbrdz/BarcenasMexico-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Danielbrdz/BarcenasMexico-270m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Danielbrdz/BarcenasMexico-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Danielbrdz/BarcenasMexico-270m with Docker Model Runner:
```
docker model run hf.co/Danielbrdz/BarcenasMexico-270m
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Barcenas México 270m

Basado en Gemma 3 270m y entrenado con el dataset Barcenas México

El objetivo de este LLM es tener modelo que sepa todo de México, su historia, cultura, gastronomía, etc. Todo en LLM accesible para todo como es Gemma 3 270m

El LLM puede contestar preguntas de México con precisión, por su entrenamiento con datos de México hecha por humanos.

Barcenas Mexico 270m

Based on Gemma 3 270m and trained with the Barcenas Mexico dataset

The objective of this LLM is to have a model that knows everything about Mexico, its history, culture, gastronomy, etc. All in an LLM accessible to everyone, such as Gemma 3 270m

The LLM can answer questions about Mexico with precision, due to its training with data from Mexico created by humans.

Made with ❤️ in Guadalupe, Nuevo Leon, Mexico 🇲🇽