metadata
license: gemma
library_name: mlx
pipeline_tag: text-generation
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extra_gated_prompt: >-
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base_model: mlx-community/gemma-3-4b-it-4bit
base_model_relation: quantized
tags:
- mlx
samairtimer/gemma-3-4b-it-4bit-HumanLike
This model samairtimer/gemma-3-4b-it-4bit-HumanLike was converted to MLX format from mlx-community/gemma-3-4b-it-4bit using mlx-lm version 0.26.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("samairtimer/gemma-3-4b-it-4bit-HumanLike")
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)