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README.md
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model-index:
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- name: TinyLlama-1.1B-DPO-Function-Calling-3T
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results: []
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---
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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<details><summary>See axolotl config</summary>
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This model is a fine-tuned version of [gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T](https://huggingface.co/gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T) on the None dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- Transformers 4.37.0
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- Pytorch 2.1.2+cu121
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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model-index:
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- name: TinyLlama-1.1B-DPO-Function-Calling-3T
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results: []
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datasets:
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- argilla/distilabel-intel-orca-dpo-pairs
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language:
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- en
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## TinyLlama-1.1B-DPO-Function-Calling-3T
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This model is a DPO fine tune of [gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T](https://huggingface.co/datasets/gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T) which itself was trained on:
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1. [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup)
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1. [gardner/glaive-function-calling-v2-sharegpt](https://huggingface.co/datasets/gardner/glaive-function-calling-v2-sharegpt)
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The model scores unusually high on GSM8K which indicates the glaive function calling dataset may introduce data contamination.
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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<details><summary>See axolotl config</summary>
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This model is a fine-tuned version of [gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T](https://huggingface.co/gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T) on the None dataset.
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### Training hyperparameters
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The following hyperparameters were used during training:
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- Transformers 4.37.0
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- Pytorch 2.1.2+cu121
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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