kathleenfromgdm

6 ms·

1. 1.▲
   by kathleenfromgdm 3y ago

   The context length for these models is 8192 tokens.
2. 2.▲
   by kathleenfromgdm 3y ago

   Good catch - just corrected. Thanks!
3. 3.▲
   by kathleenfromgdm 3y ago

   The context length for these models is 8192 tokens.
4. 4.▲
   by kathleenfromgdm 3y ago

   Great question - we compare to the Mistral 7B 0.1 pretrained models (since there were no pretrained checkpoint updates in 0.2) and the Mistral 7B 0.2 instruction-tuned models in the technical report here: https://goo.gle/Gem
5. 5.▲
   by kathleenfromgdm 3y ago

   We release our non-aligned models (marked as pretrained or PT models across platforms) alongside our fine-tuned checkpoints; for example, here is our pretrained 7B checkpoint for download: https://www.kaggle.com/models/
6. 6.▲
   by kathleenfromgdm 3y ago

   Corrected - thanks :)
7. 7.▲
   by kathleenfromgdm 3y ago

   Yes, you can get started downloading the model and running inference on Kaggle: https://www.kaggle.com/models/google/gemma ; for a full list of ways to interact with the model, you can check out https:/&#x2F
8. 8.▲
   by kathleenfromgdm 3y ago

   Thank you! You can get started downloading the model and running inference on Kaggle: https://www.kaggle.com/models/google/gemma ; for a full list of ways to interact with the model, you can check out https:&#x2F
9. 9.▲
   by kathleenfromgdm 3y ago

   We've documented the architecture (including key differences) in our technical report here ( https://goo.gle/GemmaReport ), and you can see the architecture implementation in our Git Repo ( https://github.com&#