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Gemma 4 E4B It Mlx 4Bit

The Model minkob/gemma-4-E4B-it-mlx-4Bit was converted to MLX format from google/gemma-4-E4B-it using mlx-lm version 0.31.2.

KindText modelBase modelgoogle/gemma-4-E4B-itVersionmodel

Model source

Kind
Text model
File size
3.94 GB
Base model
google/gemma-4-E4B-it
Version
vmodel
Updated
Sep 27, 2026
Downloads
16
Source
Hugging Face

Source description

minkob/gemma-4-E4B-it-mlx-4Bit

The Model minkob/gemma-4-E4B-it-mlx-4Bit was converted to MLX format from google/gemma-4-E4B-it using mlx-lm version 0.31.2.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("minkob/gemma-4-E4B-it-mlx-4Bit")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)

Sources

1 source
Hugging FacePrimaryVersion 2ca4cb44285f1795455103f7ef68036e9dfaaa7f
huggingface.co/minkob/gemma-4-E4B-it-mlx-4Bit/tree/2ca4cb44285f1795455103f7ef68036e9dfaaa7f

Verified Sep 27

Model artifacts

1 artifact

model.safetensors

safetensors · 3.94 GB · SHA-256 362dcd79a577…e133 · Hugging Face

Download

Source excerpts

2 excerpts

--- library_name: transformers license: apache-2.0 license_link: https://ai.google.dev/gemma/docs/gemma_4_license pipeline_tag: any-to-any base_model: google/gemma-4-E4B-it tags: - mlx --- # minkob/gemma-4-E4B-it-mlx-4Bit The Model [minkob/gemma-4-E4B-it-mlx-4Bit](https://huggingface.co/minkob/gemma-4-E4B-it-mlx-4Bit) was converted to MLX format from [google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it) using mlx-lm version **0.31.2**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("minkob/gemma-4-E4B-it-mlx-4Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

Source context: 16 downloads · 0 likes · Pipeline any-to-any · Library transformers · Repo minkob/gemma-4-E4B-it-mlx-4Bit