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README.md
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## Model Summary
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- **Model Type:**
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- **Language(s):** English
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- **Base Model:** [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) (License: [Apache 2.0](https://huggingface.co/tiiuae/falcon-40b#license))
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- **Dataset:** [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) (License: [Apache 2.0](https://huggingface.co/datasets/OpenAssistant/oasst1/blob/main/LICENSE))
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The model was fine-tuned in 4-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 10 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-40B-Chat-v0.1/blob/main/finetune_falcon40b_oasst1_with_bnb_peft.ipynb) for the code and hyperparams used to train the model.
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May 30, 2023
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## Quick Start
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To prompt the chat model, use the following format:
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- `accelerate`: 0.19.0
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- `bitsandbytes`: 0.39.0
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- `einops`: 0.6.1
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_dfurman__falcon-40b-openassistant-peft)
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| Metric | Value |
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| Avg. | 51.17 |
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| ARC (25-shot) | 62.63 |
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| HellaSwag (10-shot) | 85.59 |
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| MMLU (5-shot) | 57.77 |
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| TruthfulQA (0-shot) | 51.02 |
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| Winogrande (5-shot) | 81.45 |
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| GSM8K (5-shot) | 13.34 |
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| DROP (3-shot) | 6.36 |
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## Model Summary
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- **Model Type:** Causal language model (clm)
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- **Language(s):** English
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- **Base Model:** [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) (License: [Apache 2.0](https://huggingface.co/tiiuae/falcon-40b#license))
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- **Dataset:** [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) (License: [Apache 2.0](https://huggingface.co/datasets/OpenAssistant/oasst1/blob/main/LICENSE))
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The model was fine-tuned in 4-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 10 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-40B-Chat-v0.1/blob/main/finetune_falcon40b_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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May 30, 2023
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## [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_dfurman__falcon-40b-openassistant-peft)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 51.17 |
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| ARC (25-shot) | 62.63 |
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| HellaSwag (10-shot) | 85.59 |
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| MMLU (5-shot) | 57.77 |
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| TruthfulQA (0-shot) | 51.02 |
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| Winogrande (5-shot) | 81.45 |
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| GSM8K (5-shot) | 13.34 |
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| DROP (3-shot) | 6.36 |
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## Quick Start
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To prompt the chat model, use the following format:
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- `accelerate`: 0.19.0
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- `bitsandbytes`: 0.39.0
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- `einops`: 0.6.1
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