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README.md
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base_model: meta-llama/Llama-2-70b-hf
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*Note*: This model was ranked **6th** on 🤗's Open LLM Leaderboard in Aug 2023
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<div align="left">
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<img src="./assets/llama.png" width="125px">
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</div>
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This instruction model was built via parameter-efficient QLoRA finetuning of [llama-2-70b](https://huggingface.co/meta-llama/Llama-2-70b-hf) on the first 25k rows of [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin) (an open-source implementation of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)). Finetuning was executed on a single H100 (80 GB PCIe) for roughly 17 hours on the [Lambda Labs](https://cloud.lambdalabs.com/instances) platform.
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base_model: meta-llama/Llama-2-70b-hf
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<div align="center">
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<img src="./assets/llama.png" width="125px">
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</div>
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# Llama-2-70B-Instruct-v0.1
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*Note*: This model was ranked **6th** on 🤗's Open LLM Leaderboard in Aug 2023
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This instruction model was built via parameter-efficient QLoRA finetuning of [llama-2-70b](https://huggingface.co/meta-llama/Llama-2-70b-hf) on the first 25k rows of [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin) (an open-source implementation of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)). Finetuning was executed on a single H100 (80 GB PCIe) for roughly 17 hours on the [Lambda Labs](https://cloud.lambdalabs.com/instances) platform.
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