Gemma 2 9B

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Open language model from Google with 9.2 billion parameters, optimized for conversational interactions.

Overview

Gemma 2 9B

Description of the Gemma 2 9B Neural Network

Gemma 2 9B is an open language model from Google designed to execute instructions in a dialogue mode. The IT (Instruction-Tuned) version is a fine-tuned base model optimized for user interaction in a chat format.

The model was trained on 8 trillion tokens, including web data, programming code, and mathematical content. Modern techniques were used in creating Gemma 2 9B: sliding window attention, logit capping, and knowledge distillation. For final tuning for dialogue tasks, supervised fine-tuning, reinforcement learning from human feedback (RLHF), and model merging were used.

Training Approach

The base dataset of 8 trillion tokens allowed the model to absorb a wide range of linguistic and subject-matter patterns. The combination of supervised fine-tuning and RLHF ensures responses are consistent with user expectations in a dialogue format.

Technical Architecture Features

Sliding window attention allows the model to efficiently process long sequences, while knowledge distillation enables the compact version with 9.2 billion parameters to inherit the quality of larger teacher models. Logit capping further stabilizes the generation process.

Gemma 2 9B Specifications

CharacteristicValue
Parameters9.2 billion (9.2B)
Training tokens8.0 trillion (8.0T)
Release dateJune
Dialog applications
Generates responses to instructions
NLP research

Frequently asked questions

See also

Gemma 2 9B — review of Google's open language model