Instructions to use Danielbrdz/Barcenas-h-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Danielbrdz/Barcenas-h-1b with Transformers:

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

pipe = pipeline("text-generation", model="Danielbrdz/Barcenas-h-1b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Danielbrdz/Barcenas-h-1b")
model = AutoModelForCausalLM.from_pretrained("Danielbrdz/Barcenas-h-1b", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Danielbrdz/Barcenas-h-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Danielbrdz/Barcenas-h-1b"
# 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-h-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Danielbrdz/Barcenas-h-1b

SGLang

How to use Danielbrdz/Barcenas-h-1b 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-h-1b" \
    --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-h-1b",
		"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/Barcenas-h-1b" \
        --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-h-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Danielbrdz/Barcenas-h-1b with Docker Model Runner:
```
docker model run hf.co/Danielbrdz/Barcenas-h-1b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Barcenas h 1b

Basado en: ibm-granite/granite-4.0-h-1b y entrenado con el dataset: Danielbrdz/Barcenas-Cartas-Kimi

Es mi primer fine tuning a un LLM con una arquitectura híbrida de máquina de estado selectiva (SSM) basada en Mamba-2

Use el dataset de Barcenas Cartas Kimi para mejorar su creatividad en español y tenga la capacidad de escribir mejor en español que su modelo base

Ademas es un modelo pequeño que es accesible para la mayoría de equipos disponibles en el mercado

Barcenas h 1b

Based on: ibm-granite/granite-4.0-h-1b and trained with the dataset: Danielbrdz/Barcenas-Cartas-Kimi

This is my first fine-tuning of an LLM with a hybrid selective state machine (SSM) architecture based on Mamba-2.

Use the Barcenas Cartas Kimi dataset to improve your Spanish writing skills and enable you to write better in Spanish than your base model.

Furthermore, it's a small model that is accessible to most computers available on the market.

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

Downloads last month: 34

Safetensors

Model size

1B params

Tensor type

F16

Model tree for Danielbrdz/Barcenas-h-1b

Base model

ibm-granite/granite-4.0-h-1b-base

Finetuned

ibm-granite/granite-4.0-h-1b

Finetuned

(10)

this model

Quantizations

2 models

Danielbrdz
/

Barcenas-h-1b

Model tree for Danielbrdz/Barcenas-h-1b

Dataset used to train Danielbrdz/Barcenas-h-1b