Instructions to use vishalgimhan/uber-assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vishalgimhan/uber-assistant with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vishalgimhan/uber-assistant", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update model card README with dataset link
Browse files
README.md
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---
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license: apache-2.0
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tags:
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- qlora
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- finetuned
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- transformers
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---
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# Uber-assistant QLoRA Adapter
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meta-llama/Llama-3.1-8B-Instruct
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## Dataset
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Uber Annual Report 2024
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## Training Hyperparameters
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- LoRA rank: 16
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from peft import PeftModel
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base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
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model = PeftModel.from_pretrained(base_model, "
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tokenizer = AutoTokenizer.from_pretrained("
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```
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## License & Attribution
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---
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license: apache-2.0
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tags:
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- qlora
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- finetuned
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- transformers
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datasets:
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- vishalgimhan/uber-report-2024-dataset
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# Uber-assistant QLoRA Adapter
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meta-llama/Llama-3.1-8B-Instruct
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## Dataset
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Finetuned using the [Uber Annual Report 2024 Dataset](https://huggingface.co/datasets/vishalgimhan/uber-report-2024-dataset)
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## Training Hyperparameters
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- LoRA rank: 16
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from peft import PeftModel
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base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
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model = PeftModel.from_pretrained(base_model, "vishalgimhan/uber-assistant")
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tokenizer = AutoTokenizer.from_pretrained("vishalgimhan/uber-assistant")
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```
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## License & Attribution
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