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Model: thliang01/gemma-3-4B-T1-it-mlx-fp16
Source: Original Platform

README.md

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+---
+license: gemma
+language:
+- en
+- zh
+base_model: twinkle-ai/gemma-3-4B-T1-it
+library_name: transformers
+tags:
+- Taiwan
+- R.O.C
+- zhtw
+- SLM
+- Gemma-3
+- gemma3
+- mlx
+- mlx-my-repo
+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
+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
+---
+
+# thliang01/gemma-3-4B-T1-it-mlx-fp16
+
+The Model [thliang01/gemma-3-4B-T1-it-mlx-fp16](https://huggingface.co/thliang01/gemma-3-4B-T1-it-mlx-fp16) 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("thliang01/gemma-3-4B-T1-it-mlx-fp16")
+
+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)
+```