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Verified signatureThank you for reviewing the VERUMNNODE/OS model. I am submitting this pull request not only as a contribution to the open-source ecosystem but as a technical and strategic demonstration of how civic-grade, sovereign AI infrastructure can be deployed independently — with zero reliance on commercial IDEs like Replit or proprietary clouds. 🔧 Why Hugging Face Is the Right Home for VERUMNNODE This system was designed from day one to integrate tightly with diffusion-based pipelines, LoRA fine-tuning, and scalable inference — all natively supported by Hugging Face. Unlike conventional sandbox environments (e.g., Replit), Hugging Face enables: GPU-backed stable deployments Fine-tuning at scale with LoRA/SDXL Inference APIs ready for external integration Community visibility and institutional transparency 🚫 Why Not Replit? I deliberately avoided deploying via Replit due to: Severe RAM and CPU limitations under the Hacker plan (not suitable for T2I inference) Lack of GPU support or LoRA-fri…fedc5a9 -
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Verified signature# Clone o repositório (caso ainda não tenha) git clone https://huggingface.co/VERUMNNODE/OS cd OS # Crie uma nova branch para seu PR git checkout -b readme-otimizado # Edite o arquivo localmente nano README.md # ou use VSCode, etc. # Faça commit e envie git add README.md git commit -m "Otimização visual e estrutural do README.md" git push origin readme-otimizado50114a5 -
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Verified signatureimport sagemaker import boto3 from sagemaker.huggingface import HuggingFace try: role = sagemaker.get_execution_role() except ValueError: iam = boto3.client('iam') role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn'] hyperparameters = { 'model_name_or_path':'QuantFactory/diffullama-GGUF', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.49.0/path/to/script } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.49.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='train.py', source_dir='./path/to/script', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.49.0', pytorch_version='2.5.1', py_version='py311', hyperparameters = hyperparameters ) # starting the train job huggingface_estim…c71eeeb -
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Verified signature# This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # Input data files are available in the read-only "../input/" directory # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory import os for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename)) # You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session linkcode from diffusers import DiffusionPipeline import torch # Load…03ab48f -
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