small readme fix
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jphme
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README.md
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@@ -34,7 +34,7 @@ DiscoLM Mixtral 8x7b alpha is a [DiscoResearch](https://huggingface.co/DiscoRese
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1. [Download](#download)
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2. [Benchmarks](#benchmarks)
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3. [Prompt Format](#prompt-format)
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4. [Dataset](#
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5. [Acknowledgements](#acknowledgements)
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6. [Contact](#contact)
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7. [About DiscoResearch](#about-discoresearch)
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@@ -123,7 +123,7 @@ DiscoResearch is an aspiring open research community. Disco should be a place wh
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## Acknowledgements
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Many thanks first and foremost to [Mistral AI](https://huggingface.co/mistralai) for releasing another awesome model and their release strategy that is much fun for the whole community.
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Additionally, many thanks in particular to [Dmytro Dzhulgakov](https://huggingface.co/dzhulgakov) who was the first one with a running [inference implementation](https://github.com/dzhulgakov/llama-mistral), [Vik](https://huggingface.co/vikhyatk) who spotted a critical bug in our first implementation (he actually read the paper!), [winglian](https://huggingface.co/winglian) for helpful advice and Axolotl which was used to finetune the model, [
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**DiscoLM Mixtral is a [DiscoResearch](https://huggingface.co/DiscoResearch) project and was created by [Björn Plüster](https://huggingface.co/bjoernp).
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The model was trained with compute provided by [HessianAI](https://hessian.ai/); many thanks as well to [LAION](https://laion.ai) for their coordination and providing invaluable contacts + advice.**
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1. [Download](#download)
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2. [Benchmarks](#benchmarks)
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3. [Prompt Format](#prompt-format)
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4. [Dataset](#datasets)
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5. [Acknowledgements](#acknowledgements)
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6. [Contact](#contact)
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7. [About DiscoResearch](#about-discoresearch)
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## Acknowledgements
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Many thanks first and foremost to [Mistral AI](https://huggingface.co/mistralai) for releasing another awesome model and their release strategy that is much fun for the whole community.
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Additionally, many thanks in particular to [Dmytro Dzhulgakov](https://huggingface.co/dzhulgakov) who was the first one with a running [inference implementation](https://github.com/dzhulgakov/llama-mistral), [Vik](https://huggingface.co/vikhyatk) who spotted a critical bug in our first implementation (he actually read the paper!), [winglian](https://huggingface.co/winglian) for helpful advice and Axolotl which was used to finetune the model, [Migel Tissera](https://huggingface.co/migtissera), [Nous Research](https://huggingface.co/NousResearch) and [MetaMath](https://huggingface.co/meta-math) for their great datasets, and everyone who participated in this awesome speedrun on any of the Discords (please contact us if we forgot to mention you here!).
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**DiscoLM Mixtral is a [DiscoResearch](https://huggingface.co/DiscoResearch) project and was created by [Björn Plüster](https://huggingface.co/bjoernp).
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The model was trained with compute provided by [HessianAI](https://hessian.ai/); many thanks as well to [LAION](https://laion.ai) for their coordination and providing invaluable contacts + advice.**
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