lmstudio-community/gemma-2-27b-it-GGUF

💫 Community Model> Gemma 2 27b Instruct by Google

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Model creator: Google
Original model: gemma-2-27b-it
GGUF quantization: provided by bartowski based on llama.cpp release b3259

Model Settings:

Requires LM Studio 0.2.27, update can be downloaded from here: https://lmstudio.ai

Model Summary:

Gemma 2 instruct is a a brand new model from Google in the Gemma family based on the technology from Gemini. Trained on a combination of web documents, code, and mathematics, this model should excel at anything you throw at it.
With 27B parameters, this fills in a really great gap between the typical ~8B and 70B models, and should be great for anyone with moderate VRAM availability.

Prompt Template:

Choose the 'Google Gemma Instruct' preset in your LM Studio.

Under the hood, the model will see a prompt that's formatted like so:

<start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model

Note that this model does not support a System prompt.

Technical Details

Gemma 2 features the same extremely large vocabulary from release 1.1, which tends to help with multilingual and coding proficiency.

Gemma 2 27B was trained on a wide dataset of 13 trillion tokens, more than twice as many as Gemma 1.1, and an extra 60% over the 9B model, using similar datasets including:

• Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content.
• Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions.
• Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries.

For more details check out their blog post here: https://huggingface.co/blog/gemma2

Special thanks

🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.

🙏 Special thanks to Kalomaze and Dampf for their work on the dataset (linked here) that was used for calculating the imatrix for all sizes.

Disclaimers

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