Instructions to use Danielbrdz/Barcenas-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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license: llama2
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license: llama2
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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.
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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.
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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.
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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.
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Made with ❤️ in Guadalupe, Nuevo Leon, Mexico 🇲🇽
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