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metadata
library_name: transformers
license: gemma
widget:
  - messages:
      - role: user
        content: How does the brain work?
inference:
  parameters:
    max_new_tokens: 200
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
  To access Gemma on Hugging Face, you’re required to review and agree to
  Google’s usage license. To do this, please ensure you’re logged-in to Hugging
  Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-1.1-2b-it
tags:
  - TensorBlock
  - GGUF
TensorBlock

Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server

google/gemma-1.1-2b-it - GGUF

This repo contains GGUF format model files for google/gemma-1.1-2b-it.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Prompt template 

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

Model file specification

Filename Quant type File Size Description
gemma-1.1-2b-it-Q2_K.gguf Q2_K 1.158 GB smallest, significant quality loss - not recommended for most purposes
gemma-1.1-2b-it-Q3_K_S.gguf Q3_K_S 1.288 GB very small, high quality loss
gemma-1.1-2b-it-Q3_K_M.gguf Q3_K_M 1.384 GB very small, high quality loss
gemma-1.1-2b-it-Q3_K_L.gguf Q3_K_L 1.466 GB small, substantial quality loss
gemma-1.1-2b-it-Q4_0.gguf Q4_0 1.551 GB legacy; small, very high quality loss - prefer using Q3_K_M
gemma-1.1-2b-it-Q4_K_S.gguf Q4_K_S 1.560 GB small, greater quality loss
gemma-1.1-2b-it-Q4_K_M.gguf Q4_K_M 1.630 GB medium, balanced quality - recommended
gemma-1.1-2b-it-Q5_0.gguf Q5_0 1.799 GB legacy; medium, balanced quality - prefer using Q4_K_M
gemma-1.1-2b-it-Q5_K_S.gguf Q5_K_S 1.799 GB large, low quality loss - recommended
gemma-1.1-2b-it-Q5_K_M.gguf Q5_K_M 1.840 GB large, very low quality loss - recommended
gemma-1.1-2b-it-Q6_K.gguf Q6_K 2.062 GB very large, extremely low quality loss
gemma-1.1-2b-it-Q8_0.gguf Q8_0 2.669 GB very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/gemma-1.1-2b-it-GGUF --include "gemma-1.1-2b-it-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/gemma-1.1-2b-it-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'