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--- |
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license: other |
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language: |
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- en |
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pipeline_tag: text-generation |
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inference: false |
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tags: |
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- transformers |
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- gguf |
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- imatrix |
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- gemma-2-9b-it-WPO-HB |
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--- |
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Quantizations of https://huggingface.co./wzhouad/gemma-2-9b-it-WPO-HB |
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### Inference Clients/UIs |
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* [llama.cpp](https://github.com/ggerganov/llama.cpp) |
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* [KoboldCPP](https://github.com/LostRuins/koboldcpp) |
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* [ollama](https://github.com/ollama/ollama) |
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* [jan](https://github.com/janhq/jan) |
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* [text-generation-webui](https://github.com/oobabooga/text-generation-webui) |
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* [GPT4All](https://github.com/nomic-ai/gpt4all) |
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--- |
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# From original readme |
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gemma-2-9b-it finetuned by hybrid WPO, utilizing two types of data: |
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1. On-policy sampled gemma outputs based on Ultrafeedback prompts. |
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2. GPT-4-turbo outputs based on Ultrafeedback prompts. |
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In comparison to the preference data construction method in our paper, we switch to RLHFlow/ArmoRM-Llama3-8B-v0.1 to score the outputs, and choose the outputs with maximum/minimum scores to form a preference pair. |
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We provide our training data at [wzhouad/gemma-2-ultrafeedback-hybrid](https://huggingface.co./datasets/wzhouad/gemma-2-ultrafeedback-hybrid). |
