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README.md
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@@ -93,7 +93,11 @@ A quick overview of the model's strengths include:
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## Trained on Benchmarks?
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Well, yes, but actually no. You may see the names of benchmarks in the datasets used, however only **train** splits were used. If you don't know the difference, please learn.
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- [Meta AI](https://llama.meta.com/llama3/): This model would never have been possible if Meta AI did not release Llama 3 with an open license. We thank them deeply for making frontier LLMs available for all.
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- [Jon Durbin](https://huggingface.co/jondurbin): We've used many of his datasets to train this model, specifically `airoboros-3.2`, `contextual-dpo-v0.1`, `gutenberg-dpo-v0.1`, `py-dpo-v0.1`, `truthy-dpo-v0.1`, `cinematika-v0.1`, `gutenberg-dpo-v0.1`. His work is amazing and we thank him a lot. We've used a lot of datasets for our model that he used for his `bagel` series of models too. If you couldn't already guess, this model is essentially a `bagel` model but with our custom datasets and RLAIF methodology added in.
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## Trained on Benchmarks?
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Well, yes, but actually no. You may see the names of benchmarks in the datasets used, however only **train** splits were used. If you don't know the difference, please learn.
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## Quants and Other Formats
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- GGUFs: [https://huggingface.co/darkcloudai/huskylm-2.5-8b-GGUF](https://huggingface.co/darkcloudai/huskylm-2.5-8b-GGUF)
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- AWQ (bits: 4, gs: 128, version: gemm): [https://huggingface.co/darkcloudai/huskylm-2.5-8b-AWQ](https://huggingface.co/darkcloudai/huskylm-2.5-8b-AWQ)
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## Huge Thank You to the Following People/Companies
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- [Meta AI](https://llama.meta.com/llama3/): This model would never have been possible if Meta AI did not release Llama 3 with an open license. We thank them deeply for making frontier LLMs available for all.
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- [Jon Durbin](https://huggingface.co/jondurbin): We've used many of his datasets to train this model, specifically `airoboros-3.2`, `contextual-dpo-v0.1`, `gutenberg-dpo-v0.1`, `py-dpo-v0.1`, `truthy-dpo-v0.1`, `cinematika-v0.1`, `gutenberg-dpo-v0.1`. His work is amazing and we thank him a lot. We've used a lot of datasets for our model that he used for his `bagel` series of models too. If you couldn't already guess, this model is essentially a `bagel` model but with our custom datasets and RLAIF methodology added in.
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