Instructions to use Animateus/gemma-4-E2B-5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Animateus/gemma-4-E2B-5bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Animateus/gemma-4-E2B-5bit") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use Animateus/gemma-4-E2B-5bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "Animateus/gemma-4-E2B-5bit" --prompt "Once upon a time"
- Atomic Chat
gemma-4-E2B-5bit
MLX-format, 5-bit quantization of Google's google/gemma-4-E2B base (non-instruction-tuned)
checkpoint, converted with mlx_lm.convert for use with MLX
and MLX-Swift on Apple Silicon.
This is not an official Google release. "Gemma" is a trademark of Google LLC; this repository is not affiliated with, endorsed by, or sponsored by Google.
What was changed from upstream
Quantized from google/gemma-4-E2B to 5-bit precision (group size 64) using
mlx_lm.convert -q --q-bits 5, mlx-lm version 0.31.3. No other modification was made —
architecture, tokenizer, and generation config are unchanged from upstream.
License
Distributed under the Apache License 2.0 (see LICENSE in this repository), matching
the license Google publishes google/gemma-4-E2B under.
Google additionally applies use restrictions to the Gemma family that are not waived by the Apache License tag and continue to apply to these weights regardless of who redistributes them:
By downloading or using this checkpoint you agree to comply with the Prohibited Use Policy.
See NOTICE for the upstream attribution this repository carries forward.
Usage
pip install -U mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Animateus/gemma-4-E2B-5bit")
prompt = "The quick brown fox"
response = generate(model, tokenizer, prompt=prompt, verbose=True)
This is a base (non-instruction-tuned) checkpoint — prompt it with raw text to continue, not with chat-formatted instructions.
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Model tree for Animateus/gemma-4-E2B-5bit
Base model
google/gemma-4-E2B