---
license: gemma
language:
- en
- zh
base_model: twinkle-ai/gemma-3-4B-T1-it
library_name: mlx
tags:
- Taiwan
- R.O.C
- zhtw
- SLM
- Gemma-3
- gemma3
- mlx
datasets:
- lianghsun/tw-reasoning-instruct
- lianghsun/tw-contract-review-chat
- minyichen/tw-instruct-R1-200k
- minyichen/tw_mm_R1
- minyichen/LongPaper_multitask_zh_tw_R1
- nvidia/Nemotron-Instruction-Following-Chat-v1
metrics:
- accuracy
pipeline_tag: text-generation
model-index:
- name: gemma-3-4B-T1-it
  results:
  - task:
      type: question-answering
      name: Single Choice Question
    dataset:
      name: tmmlu+
      type: ikala/tmmluplus
      config: all
      split: test
      revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c
    metrics:
    - type: accuracy
      value: 47.44
      name: single choice
  - task:
      type: question-answering
      name: Single Choice Question
    dataset:
      name: mmlu
      type: cais/mmlu
      config: all
      split: test
      revision: c30699e
    metrics:
    - type: accuracy
      value: 59.13
      name: single choice
  - task:
      type: question-answering
      name: Single Choice Question
    dataset:
      name: tw-legal-benchmark-v1
      type: lianghsun/tw-legal-benchmark-v1
      config: all
      split: test
      revision: 66c3a5f
    metrics:
    - type: accuracy
      value: 44.18
      name: single choice
---

# SpockH/gemma-3-4B-T1-it-mlx-4bit

This model [SpockH/gemma-3-4B-T1-it-mlx-4bit](https://huggingface.co/SpockH/gemma-3-4B-T1-it-mlx-4bit) was
converted to MLX format from [twinkle-ai/gemma-3-4B-T1-it](https://huggingface.co/twinkle-ai/gemma-3-4B-T1-it)
using mlx-lm version **0.29.1**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("SpockH/gemma-3-4B-T1-it-mlx-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)
```