mlx-community/translategemma-4b-it-4bit

Model

9

stars

26

commits

1

repos using this model

1

linked in READMEs

Jan 16, 2026

updated

4-bit
conversational
gemma3
mlx
safetensors
text-generation
Browse cluster: Multimodal Language Models & Quantization

README

mlx-community/translategemma-4b-it-4bit

This model mlx-community/translategemma-4b-it-4bit was converted to MLX format from google/translategemma-4b-it using mlx-lm version 0.29.1.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/translategemma-4b-it-4bit")

prompt = "hello"

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

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

Contributors

schroneko

26 commits

mlx-community/translategemma-4b-it-4bit

Model

9

stars

26

commits

1

repos using this model

1

linked in READMEs

Jan 16, 2026

updated

4-bit
conversational
gemma3
mlx
safetensors
text-generation
Browse cluster: Multimodal Language Models & Quantization

README

mlx-community/translategemma-4b-it-4bit

This model mlx-community/translategemma-4b-it-4bit was converted to MLX format from google/translategemma-4b-it using mlx-lm version 0.29.1.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/translategemma-4b-it-4bit")

prompt = "hello"

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

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

Contributors

schroneko

26 commits