license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
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Google’s usage license. To do this, please ensure you’re logged in to Hugging
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tags:
- conversational
quantized_by: bartowski
lm_studio:
param_count: 27b
use_case: general
release_date: 27-06-2024
model_creator: google
prompt_template: Google Gemma Instruct
system_prompt: none
base_model: gemma
original_repo: google/gemma-2-27b-it
base_model: google/gemma-2-27b-it
💫 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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