Instructions to use Danielbrdz/Barcenas-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Danielbrdz/Barcenas-3b with Transformers:

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

pipe = pipeline("text-generation", model="Danielbrdz/Barcenas-3b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Danielbrdz/Barcenas-3b")
model = AutoModelForCausalLM.from_pretrained("Danielbrdz/Barcenas-3b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Danielbrdz/Barcenas-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Danielbrdz/Barcenas-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Danielbrdz/Barcenas-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Danielbrdz/Barcenas-3b

SGLang

How to use Danielbrdz/Barcenas-3b 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-3b" \
    --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": "Danielbrdz/Barcenas-3b",
		"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 "Danielbrdz/Barcenas-3b" \
        --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": "Danielbrdz/Barcenas-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Danielbrdz/Barcenas-3b with Docker Model Runner:
```
docker model run hf.co/Danielbrdz/Barcenas-3b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Introducing Barcenas 3b, a cutting-edge AI model designed for text generation. Built upon the powerful GeneZC/MiniMA-3B architecture, this state-of-the-art model has been meticulously trained using data curated from HuggingFaceH4/no_robots. Barcenas 3b showcases remarkable capabilities in generating coherent and contextually relevant text, making it a versatile tool for a wide range of applications.

The underlying GeneZC/MiniMA-3B architecture provides a robust foundation for natural language understanding and expression. Leveraging advanced techniques in machine learning, Barcenas 3b excels in producing human-like text, capturing nuances and intricacies to deliver content that resonates with users.

The training data, sourced from HuggingFaceH4/no_robots, ensures that Barcenas 3b is attuned to real-world language patterns, enabling it to generate text that reflects contemporary linguistic nuances and styles. This diverse dataset contributes to the model's adaptability across various domains and industries.

Whether used for creative writing, content generation, or other text-based tasks, Barcenas 3b stands out as a reliable and innovative AI model. Its proficiency in understanding and generating contextually appropriate text sets it apart in the realm of natural language processing, offering users a powerful tool for enhancing their applications and workflows.

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

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