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Falcon-40b-chat-oasst1 is a chatbot-like model for dialogue generation. It was built by fine-tuning [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) on the [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) dataset.
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- The model was fine-tuned in 4-bit precision using `peft`, `transformers`, and `bitsandbytes`.
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- The training relied on a method called "Low Rank Adapters" ([LoRA](https://arxiv.org/pdf/2106.09685.pdf)), specifically the [QLoRA](https://arxiv.org/abs/2305.14314) variant. Instead of fine-tuning the entire model you fine-tune lightweight adapters and load them inside the base model at inference.
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- Fine-tuning took approximately 10 hours and was executed on a workstation with a single A100-SXM NVIDIA GPU, with 37 GB of available memory
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- See attached [Colab Notebook](https://huggingface.co/dfurman/falcon-40b-chat-oasst1/blob/main/finetune_falcon40b_oasst1_with_bnb_peft.ipynb) for the code and hyperparams used to train the model.
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## Model Summary
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Falcon-40b-chat-oasst1 is a chatbot-like model for dialogue generation. It was built by fine-tuning [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) on the [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) dataset.
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- The model was fine-tuned in 4-bit precision using `peft`, `transformers`, and `bitsandbytes`.
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- The training relied on a method called "Low Rank Adapters" ([LoRA](https://arxiv.org/pdf/2106.09685.pdf)), specifically the [QLoRA](https://arxiv.org/abs/2305.14314) variant. Instead of fine-tuning the entire model you fine-tune lightweight adapters and load them inside the base model at inference.
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- Fine-tuning took approximately 10 hours and was executed on a workstation with a single A100-SXM NVIDIA GPU, with 37 GB of available memory
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- See attached [Colab Notebook](https://huggingface.co/dfurman/falcon-40b-chat-oasst1/blob/main/finetune_falcon40b_oasst1_with_bnb_peft.ipynb) for the code and hyperparams used to train the model.
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## Model Summary
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