Instructions to use AiHub4MSRH-Hash/hash-MedGemma-1.5-4B-8bit-multilingual-it-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AiHub4MSRH-Hash/hash-MedGemma-1.5-4B-8bit-multilingual-it-3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AiHub4MSRH-Hash/hash-MedGemma-1.5-4B-8bit-multilingual-it-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from AiHub4MSRH-Hash/hash-MedGemma-1.5-4B-8bit-multilingual-it-3: direct link, hf CLI and curl.
- Browser
- Download file 584 Bytes
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https://huggingface.co/AiHub4MSRH-Hash/hash-MedGemma-1.5-4B-8bit-multilingual-it-3/resolve/main/README.md
- Command line
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hf download hf://AiHub4MSRH-Hash/hash-MedGemma-1.5-4B-8bit-multilingual-it-3/README.md
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curl -L -o README.md https://huggingface.co/AiHub4MSRH-Hash/hash-MedGemma-1.5-4B-8bit-multilingual-it-3/resolve/main/README.md
584 Bytes
metadata
base_model: google/medgemma-1.5-4b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
Uploaded finetuned model
- Developed by: AiHub4MSRH-Hash
- License: apache-2.0
- Finetuned from model : google/medgemma-1.5-4b-it
This gemma3 model was trained 2x faster with Unsloth and Huggingface's TRL library.
