Instructions to use belal212/Fattah-Orchestrator-E2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use belal212/Fattah-Orchestrator-E2B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("belal212/Fattah-Orchestrator-E2B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from belal212/Fattah-Orchestrator-E2B: direct link, hf CLI and curl.
- Browser
- Download file 589 Bytes
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https://huggingface.co/belal212/Fattah-Orchestrator-E2B/resolve/main/README.md
- Command line
-
hf download hf://belal212/Fattah-Orchestrator-E2B/README.md
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curl -L -o README.md https://huggingface.co/belal212/Fattah-Orchestrator-E2B/resolve/main/README.md
589 Bytes
metadata
base_model: nomeda-lab/Fattah-E2B-Translate
tags:
- text-generation-inference
- transformers
- unsloth
- gemma4
license: apache-2.0
language:
- en
Uploaded finetuned model
- Developed by: belal212
- License: apache-2.0
- Finetuned from model : nomeda-lab/Fattah-E2B-Translate
This gemma4 model was trained 2x faster with Unsloth and Huggingface's TRL library.
