95 lines
2.1 KiB
Markdown
95 lines
2.1 KiB
Markdown
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---
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license: gemma
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language:
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- en
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- zh
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base_model: twinkle-ai/gemma-3-4B-T1-it
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library_name: transformers
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tags:
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- Taiwan
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- R.O.C
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- zhtw
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- SLM
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- Gemma-3
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- gemma3
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- mlx
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- mlx-my-repo
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datasets:
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- lianghsun/tw-reasoning-instruct
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- lianghsun/tw-contract-review-chat
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- minyichen/tw-instruct-R1-200k
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- minyichen/tw_mm_R1
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- minyichen/LongPaper_multitask_zh_tw_R1
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- nvidia/Nemotron-Instruction-Following-Chat-v1
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metrics:
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- accuracy
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model-index:
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- name: gemma-3-4B-T1-it
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results:
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- task:
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type: question-answering
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name: Single Choice Question
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dataset:
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name: tmmlu+
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type: ikala/tmmluplus
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config: all
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split: test
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c
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metrics:
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- type: accuracy
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value: 47.44
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name: single choice
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- task:
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type: question-answering
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name: Single Choice Question
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dataset:
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name: mmlu
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type: cais/mmlu
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config: all
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split: test
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revision: c30699e
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metrics:
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- type: accuracy
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value: 59.13
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name: single choice
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- task:
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type: question-answering
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name: Single Choice Question
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dataset:
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name: tw-legal-benchmark-v1
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type: lianghsun/tw-legal-benchmark-v1
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config: all
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split: test
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revision: 66c3a5f
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metrics:
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- type: accuracy
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value: 44.18
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name: single choice
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---
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# thliang01/gemma-3-4B-T1-it-mlx-fp16
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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**.
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("thliang01/gemma-3-4B-T1-it-mlx-fp16")
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prompt="hello"
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if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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