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README.md
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## Model Details
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The model was fine-tuned in 8-bit precision using 🤗 `peft` adapters, `transformers`, and `bitsandbytes`. 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. The run took approximately 6.25 hours and was executed on a workstation with a single A100-SXM NVIDIA GPU with 37 GB of available memory. See attached [Colab Notebook](https://huggingface.co/dfurman/falcon-7b-
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### Model Date
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# load the model
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peft_model_id = "dfurman/falcon-7b-
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config = PeftConfig.from_pretrained(peft_model_id)
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model = AutoModelForCausalLM.from_pretrained(
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## Reproducibility
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See attached [Colab Notebook](https://huggingface.co/dfurman/falcon-7b-
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### CUDA Info
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## Model Details
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The model was fine-tuned in 8-bit precision using 🤗 `peft` adapters, `transformers`, and `bitsandbytes`. 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. The run took approximately 6.25 hours and was executed on a workstation with a single A100-SXM NVIDIA GPU with 37 GB of available memory. See attached [Colab Notebook](https://huggingface.co/dfurman/falcon-7b-openassistant-peft/blob/main/finetune_falcon7b_oasst1_with_bnb_peft.ipynb) for the code and hyperparams used to train the model.
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### Model Date
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# load the model
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peft_model_id = "dfurman/falcon-7b-openassistant-peft"
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config = PeftConfig.from_pretrained(peft_model_id)
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model = AutoModelForCausalLM.from_pretrained(
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## Reproducibility
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See attached [Colab Notebook](https://huggingface.co/dfurman/falcon-7b-openassistant-peft/blob/main/finetune_falcon7b_oasst1_with_bnb_peft.ipynb) for the code (and hyperparams) used to train the model.
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### CUDA Info
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