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@@ -141,6 +141,8 @@ We would like to thank the dataset providers and the open-source community whose
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  - **CAIS** for the **MMLU** dataset used for auxiliary knowledge and reasoning supervision.
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  - **HuggingFaceTB** for the **OpenHermes-2.5-H4** dataset used in the final instruction refinement phase.
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  - **Weights & Biases (W&B)** for logging and visualization tools used to monitor training progress.
 
 
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  We also acknowledge the broader open-source research community for their continuous efforts in advancing efficient model architectures and training methodologies.
 
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  - **CAIS** for the **MMLU** dataset used for auxiliary knowledge and reasoning supervision.
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  - **HuggingFaceTB** for the **OpenHermes-2.5-H4** dataset used in the final instruction refinement phase.
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  - **Weights & Biases (W&B)** for logging and visualization tools used to monitor training progress.
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+ - Additionally, we drew valuable insights from **The Smol Training Playbook: The Secrets to Building World-Class LLMs**, published by Hugging Face, which informed several practical decisions in our training and experimentation workflow.
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+ Playbook link: https://huggingfacetb-smol-training-playbook.hf.space/the-smol-training-playbook-the-secrets-to-building-world-class-llms.pdf
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  We also acknowledge the broader open-source research community for their continuous efforts in advancing efficient model architectures and training methodologies.