content.raw: Introducing: Zephyr Gemma!
content.type: text
content.value: Introducing: Zephyr Gemma!
content.type: new_line
content.type: new_line
content.raw: The community has struggled to do a good preference-tune of Gemma, so the amazing 
content.type: text
content.value: The community has struggled to do a good preference-tune of Gemma, so the amazing 
content.raw: @lewtun
content.type: mention
content.user: lewtun
content.raw:  and 
content.type: text
content.value:  and 
content.raw: @philschmid
content.type: mention
content.user: philschmid
content.raw:  built an open-source recipe and trained a model to help people get started. 
content.type: text
content.value:  built an open-source recipe and trained a model to help people get started. 
content.type: new_line
content.type: new_line
content.raw: Handbook: 
content.type: text
content.value: Handbook: 
content.href: https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-gemma/README.md
content.raw: https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-gemma/README.md
content.type: link
content.type: new_line
content.raw: Model: 
content.type: text
content.value: Model: 
content.raw: https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1
content.id: HuggingFaceH4/zephyr-7b-gemma-v0.1
content.type: model
content.type: resource
content.url: https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1
content.type: new_line
content.raw: Demo: 
content.type: text
content.value: Demo: 
content.raw: https://huggingface.co/spaces/HuggingFaceH4/zephyr-7b-gemma-chat
content.id: HuggingFaceH4/zephyr-7b-gemma-chat
content.type: space
content.type: resource
content.url: https://huggingface.co/spaces/HuggingFaceH4/zephyr-7b-gemma-chat
content.type: new_line
content.type: new_line
content.raw: Some interesting details
content.type: text
content.value: Some interesting details
content.type: new_line
content.raw: - Fine-tuned on DEITA and DPOed with Argilla DPO dataset
content.type: text
content.value: - Fine-tuned on DEITA and DPOed with Argilla DPO dataset
content.type: new_line
content.raw: - Very strong MT Bench results (7.81), better than Zephyr Beta (mistral based) and Gemma Instruct
content.type: text
content.value: - Very strong MT Bench results (7.81), better than Zephyr Beta (mistral based) and Gemma Instruct
content.type: new_line
content.raw: - Can run locally with tools such as llama.cpp on a Mac
content.type: text
content.value: - Can run locally with tools such as llama.cpp on a Mac
content.type: new_line
content.raw: - Not so good AGIEval results compared to mistral-based tunes
content.type: text
content.value: - Not so good AGIEval results compared to mistral-based tunes
content.type: new_line
content.raw: - All training code is open-sourced
content.type: text
content.value: - All training code is open-sourced
content.type: new_line
content.raw: - Trained for 105 minutes on 8x H100
content.type: text
content.value: - Trained for 105 minutes on 8x H100
content.type: new_line
content.raw: - No system message
content.type: text
content.value: - No system message
content.type: new_line
content.type: new_line
content.raw: Big kudos to the team! Super exciting to see a good fine-tune for Gemma
content.type: text
content.value: Big kudos to the team! Super exciting to see a good fine-tune for Gemma
rawContent: Introducing: Zephyr Gemma!
  
  The community has struggled to do a good preference-tune of Gemma, so the amazing @lewtun and @philschmid built an open-source recipe and trained a model to help people get started. 
  
  Handbook: https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-gemma/README.md
  Model: https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1
  Demo: https://huggingface.co/spaces/HuggingFaceH4/zephyr-7b-gemma-chat
  
  Some interesting details
  - Fine-tuned on DEITA and DPOed with Argilla DPO dataset
  - Very strong MT Bench results (7.81), better than Zephyr Beta (mistral based) and Gemma Instruct
  - Can run locally with tools such as llama.cpp on a Mac
  - Not so good AGIEval results compared to mistral-based tunes
  - All training code is open-sourced
  - Trained for 105 minutes on 8x H100
  - No system message
  
  Big kudos to the team! Super exciting to see a good fine-tune for Gemma
reactions.reaction: 🤗
reactions.users: yjernite vladbogo clem samusenps dkyazze taufiqdp pdina
reactions.reaction: ❤️
reactions.users: clem samusenps ajibawa-2023 giux78
reactions.reaction: 🤝
reactions.users: clem
commentators._id: 64aea8ff67511bd3d965697b
commentators.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64aea8ff67511bd3d965697b/Jxn52EmDF5RApJh8antxn.jpeg
commentators.fullname: Feynman Innovations
commentators.name: ajibawa-2023
commentators.type: user