Slowblood Python Module

This python package will have a collection of structures, functions and tools for interacting with LLMs and Datasets.

Project and PyPI Package

• https://pypi.org/project/slowblood/
• https://github.com/kyledinh/slowblood
• https://huggingface.co/Slowblood

Usage Examples

Get RunPod Available GPUs with price

import slowblood
slowblood.lib_runpod.runpod_info(RUNPOD_API_KEY)

# or if you have the RUNPOD_API_KEY set in .env file
slowblood.runpod_get_available_gpus()

output:

GPUs: 
{'maxGpuCount': 8, 'id': 'NVIDIA A100 80GB PCIe', 'displayName': 'A100 80GB', 'manufacturer': 'Nvidia', 'memoryInGb': 80, 'cudaCores': 0, 'secureCloud': True, 'communityCloud': True, 'securePrice': 1.89, 'communityPrice': 1.59, 'oneMonthPrice': None, 'threeMonthPrice': None, 'oneWeekPrice': None, 'communitySpotPrice': 0.89, 'secureSpotPrice': None, 'lowestPrice': {'minimumBidPrice': 0.89, 'uninterruptablePrice': 1.59}} ...

Copy Save HuggingFace Model

Copy a Hugging Face model to a new Hugging Face Org

Required Dependencies

pip install -qU git+https://github.com/huggingface/transformers.git
pip install -qU git+https://github.com/huggingface/peft.git
pip install -qU git+https://github.com/huggingface/accelerate.git

Package Structs, Methods and Consts

Datasets

• TextDataset
• prepare_dataset_llama2()
• generate_from_dataset_llama2()

Model

• BITS_AND_BYTES_CONFIG_4BIT
• load_peft_model_with_adapters()
• print_trainable_parameters()

PDF Manipulation

• convert_pdf_to_images()
• extract_text_from_pdf()
• extract_text_from_imgs()

Settings

• BasicInferenceRequest
• ArtifactNames
• FineTuningSettings

Tokenizer

• get_tokenizer_for_model()
• update_model_with_tokenizer()