Instructions to use Danielbrdz/Barcenas-31b-Fable with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Danielbrdz/Barcenas-31b-Fable with Transformers:

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

pipe = pipeline("image-text-to-text", model="Danielbrdz/Barcenas-31b-Fable")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Danielbrdz/Barcenas-31b-Fable")
model = AutoModelForMultimodalLM.from_pretrained("Danielbrdz/Barcenas-31b-Fable", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Danielbrdz/Barcenas-31b-Fable with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Danielbrdz/Barcenas-31b-Fable"
# 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/Barcenas-31b-Fable",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Danielbrdz/Barcenas-31b-Fable

SGLang

How to use Danielbrdz/Barcenas-31b-Fable 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/Barcenas-31b-Fable" \
    --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/Barcenas-31b-Fable",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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/Barcenas-31b-Fable" \
        --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/Barcenas-31b-Fable",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Danielbrdz/Barcenas-31b-Fable with Docker Model Runner:
```
docker model run hf.co/Danielbrdz/Barcenas-31b-Fable
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Barcenas 31b Fable

Basado en Gemma 4 31b it de Google y entranado con el dataset agentic-distill-fable-5-sft

Es mi modelo más grande y potente que he hecho hasta la fecha y entrenado con datos de alta calidad directamente de Claude Fable 5 para uso de agentes

Con está combinación se puede tener un LLM que se puede ejecutar una sola GPU y para uso agéntico de manera local y privado de manera eficiente, claro si tienes los requisitos de hardware

Barcenas 31b Fable

Based on Google's Gemma 4 31b it and trained with the agentic-distill-fable-5-sft dataset.

This is my largest and most powerful model to date, trained with high-quality data directly from Claude Fable 5 for agent use.

With this combination, you can have an LLM that can run on a single GPU and implement agentic use locally and privately, provided you meet the hardware requirements.

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