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