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--- |
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license: llama3.2 |
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language: |
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- en |
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- zh |
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base_model: |
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- meta-llama/Llama-3.2-3B |
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- lianghsun/Llama-3.2-3B-F1-Base |
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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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- Llama-32 |
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datasets: |
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- lianghsun/tw-reasoning-instruct |
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- minyichen/tw-instruct-R1-200k |
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- minyichen/tw_mm_R1 |
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model-index: |
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- name: Llama-3.2-3B-F1-Reasoning-Instruct |
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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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type: ikala/tmmluplus |
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name: tmmlu+ |
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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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- name: single choice |
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type: accuracy |
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value: 46.16 |
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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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type: cais/mmlu |
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name: 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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- name: single choice |
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type: accuracy |
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value: 51.22 |
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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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type: lianghsun/tw-legal-benchmark-v1 |
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name: 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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- name: single choice |
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type: accuracy |
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value: 34.92 |
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metrics: |
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- accuracy |
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--- |
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# Model Card for Llama-3.2-3B-F1-Reasoning-Instruct (a.k.a __Formosa-1-Reasoning__ or __F1-Reasoning__) |
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<div align="center" style="line-height: 1;"> |
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<a href="https://discord.gg/Cx737yw4ed" target="_blank" style="margin: 2px;"> |
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<img alt="Discord" src="https://img.shields.io/badge/Discord-Twinkle%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> |
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</a> |
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<a href="https://huggingface.co/twinkle-ai" target="_blank" style="margin: 2px;"> |
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<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Twinkle%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> |
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</a> |
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</div> |
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<div align="center" style="line-height: 1;"> |
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<a href="https://huggingface.co/meta-llama/Llama-3.2-1B/blob/main/LICENSE.txt" style="margin: 2px;"> |
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<img alt="License" src="https://img.shields.io/badge/License-llama3.2-f5de53?&color=0081fb" style="display: inline-block; vertical-align: middle;"/> |
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</a> |
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</div> |
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 |
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<!-- Provide a quick summary of what the model is/does. --> |
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**Llama-3.2-3B-F1-Reasoning-Instruct**（a.k.a **Formosa-1-Reasoning** or **F1-Reasoning**） 是由 **[Twinkle AI](https://huggingface.co/twinkle-ai)** 與 **[APMIC](https://www.apmic.ai/)** 合作開發，並在[國家高速網路與計算中心](https://www.nchc.org.tw/)技術指導之下，針對中華民國台灣語境與任務需求所微調之繁體中文語言模型，涵蓋法律、教育、生活應用等多元場景，並以高指令跟隨能力為目標進行強化。 |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [Liang Hsun Huang](https://huggingface.co/lianghsun)、[Min Yi Chen](https://huggingface.co/minyichen)、[Wen Bin Lin](https://huggingface.co/tedslin)、[Chao Chun Chuang](https://huggingface.co/c00cjz00) & [Dave Sung](https://huggingface.co/k1dave6412) (All authors have contributed equally to this work.) |
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- **Funded by:** [APMIC](https://www.apmic.ai/) |
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- **Model type:** LlamaForCausalLM |
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- **Language(s) (NLP):** Tranditional Chinese & English |
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- **License:** [llama3.2](https://huggingface.co/meta-llama/Llama-3.2-1B/blob/main/LICENSE.txt) |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [twinkle-ai/Llama-3.2-3B-F1-Reasoning-Instruct](https://huggingface.co/twinkle-ai/Llama-3.2-3B-F1-Reasoning-Instruct) |
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- **Paper:** (TBA) |
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- **Demo:** [Playground](https://3b02.coolify.apmic.ai/) |
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## Evaluation |
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### Results |
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下表採用 [🌟 Twinkle Eval](https://github.com/ai-twinkle/Eval) 評測框架 |
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| 模型 | 評測模式 | TMMLU+(%) | 台灣法律(%) | MMLU(%) | 測試次數 | 選項排序 | |
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|------------------------------------|---------|----------------|----------------|----------------|---------|---------| |
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| [mistralai/Mistral-Small-24B-Instruct-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501) | box | 56.15 (±0.0172) | 37.48 (±0.0098) | 74.61 (±0.0154) | 3 | 隨機 | |
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| [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) | box | 15.49 (±0.0104) | 25.68 (±0.0200) | 6.90 (±0.0096) | 3 | 隨機 | |
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| [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) | pattern | 35.85 (±0.0174) | 32.22 (±0.0023) | 59.33 (±0.0168) | 3 | 隨機 | |
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| [MediaTek-Research/Llama-Breeze2-3B-Instruct](https://huggingface.co/MediaTek-Research/Llama-Breeze2-3B-Instruct) | pattern | 40.32 (±0.0181) | 38.92 (±0.0193) | 55.37 (±0.0180) | 3 | 隨機 | |
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| [twinkle-ai/Llama-3.2-3B-F1-Reasoning-Instruct](https://huggingface.co/twinkle-ai/Llama-3.2-3B-F1-Reasoning-Instruct) (ours) | box | 46.16 (±0.0198) | 34.92 (±0.0243) | 51.22 (±0.0206) | 3 | 隨機 | |
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下表用 lighteval 評測框架 |
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| 模型 | MATH-500 | GPQA Diamond | |
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|--------------------------------------------|----------|--------------| |
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| [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) | 44.40 | 27.78 | |
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| [twinkle-ai/Llama-3.2-3B-F1-Reasoning-Instruct](https://huggingface.co/twinkle-ai/Llama-3.2-3B-F1-Reasoning-Instruct) (ours) | **51.40**| **33.84** | |
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--- |
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## 🔧 Tool Calling |
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本模型使用 Hermes 格式訓練，並支援平行呼叫（Parallel calling），以下為完整範例流程。 |
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Tool call 模板已經為大家寫好放進 chat-template 了，Enjoy it！ |
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### 1️⃣ 啟動 vLLM 後端 |
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> **⚠️ 注意：需要 vLLM 版本 >= 0.8.3，否則 `enable-reasoning`、`enable-auto-tool-choice` 無法同時開啟** |
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```bash |
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vllm serve twinkle-ai/Llama-3.2-3B-F1-Reasoning-Instruct \ |
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--port 8001 \ |
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--enable-reasoning \ |
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--reasoning-parser deepseek_r1 \ |
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--enable-auto-tool-choice \ |
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--tool-call-parser hermes |
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``` |
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### 2️⃣ 定義工具（Functions） |
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```python |
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def get_weather(location: str, unit: str): |
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return f"{location}的氣溫是{unit}26度，晴朗無風" |
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def search(query: str): |
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return "川普終於宣布對等關稅政策，針對 18 個經濟體課徵一半的對等關稅，並從 4/5 起對所有進口產品徵收10%的基準關稅！美國將針對被認定為不當貿易行為(不公平貿易) 的國家，於 4/9 起課徵報復型對等關稅 (Discounted Reciprocal Tariff)，例如：日本將被課徵 24% 的關稅，歐盟則為 20%，以取代普遍性的 10% 關稅。\n針對中國則開啟新一波 34% 關稅，並疊加於先前已實施的關稅上，這將使中國進口商品的基本關稅稅率達到 54%，而且這尚未包含拜登總統任內或川普第一任期所施加的額外關稅。加拿大與墨西哥則不適用這套對等關稅制度，但川普認為這些國家在芬太尼危機與非法移民問題尚未完全解決，因此計畫對這兩國的大多數進口商品施加 25% 關稅。另外原本針對汽車與多數其他商品的關稅豁免將於 4/2 到期。\n台灣的部分，美國擬向台灣課徵32％的對等關稅，雖然並未針對晶片特別課徵關稅，但仍在記者會中提到台灣搶奪所有的電腦與半導體晶片，最終促成台積電對美國投資計劃額外加碼 1,000 億美元的歷史性投資；歐盟則課徵20％的對等關稅。最後是汽車關稅將於 4/2 起，對所有外國製造的汽車課徵25% 關稅。" |
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tools = [ |
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{ |
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"type": "function", |
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"function": { |
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"name": "get_weather", |
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"description": "Get the current weather in a given location", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"location": {"type": "string", "description": "國家或城市名, e.g., 'Taipei'、'Jaipei'"}, |
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"unit": {"type": "string", "description": "氣溫單位，亞洲城市使用攝氏；歐美城市使用華氏", "enum": ["celsius", "fahrenheit"]} |
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}, |
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"required": ["location", "unit"] |
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} |
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} |
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}, |
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{ |
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"type": "function", |
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"function": { |
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"name": "search", |
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"description": "這是一個類似 Google 的搜尋引擎，關於知識、天氣、股票、電影、小說、百科等等問題，如果你不確定答案就搜尋一下。", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"query": {"type": "string", "description": "should be a search query, e.g., '2024 南韓 戒嚴'"} |
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}, |
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"required": ["query"] |
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} |
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} |
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} |
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] |
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``` |
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### 3️⃣ 執行工具調用（Tool Calls） |
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> **⚠️ 注意：system_prompt 可以不用帶，除非是需要時間基準的工具。** |
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```python |
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response = client.chat.completions.create( |
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model=client.models.list().data[0].id, |
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messages=[ |
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{"role": "system", "content": "記住你的知識截止於 2024/12，今天是 2025/4/7"}, |
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{"role": "user", "content": "台北氣溫如何? 另外，告訴我川普最新關稅政策"}, |
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], |
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max_tokens=1500, |
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temperature=0.6, |
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top_p=0.95, |
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tools=tools, |
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tool_choice="auto" |
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) |
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print(response.choices[0].message.reasoning_content) |
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print(response.choices[0].message.tool_calls) |
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``` |
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#### 🧠 推理內容輸出（僅顯示部分） |
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> 好的，我需要幫助這個使用者解決他們的問題。他們問了兩件事：首先，臺北市的天氣情況，以及第二，關於川普最近的關稅政策。 |
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> 對於第一部分，他們提到了“臺北”，所以應該呼叫 get_weather 函式… |
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> 接下來是關於川普的新關稅政策… |
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> 總結一下，我需要分別進行兩次 API 呼叫，每次都有各自正確填寫的參數… |
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#### ⚙️ Tool Calls List |
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```json |
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[ChatCompletionMessageToolCall(id='chatcmpl-tool-35e74420119349999913a10133b84bd3', function=Function(arguments='{"location": "Taipei", "unit": "celsius"}', name='get_weather'), type='function'), ChatCompletionMessageToolCall(id='chatcmpl-tool-7ffdcb98e59f4134a6171defe7f2e31b', function=Function(arguments='{"query": "Donald Trump latest tariffs policy"}', name='search'), type='function')] |
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``` |
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### 4️⃣ 產生最終回答 |
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```python |
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response = client.chat.completions.create( |
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model=client.models.list().data[0].id, |
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messages=[ |
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{"role": "system", "content": "記住你的知識截止於 2024/12，今天是 2025/4/7"}, |
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{"role": "user", "content": "台北氣溫如何? 另外，告訴我川普最新關稅政策"}, |
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{ |
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"role": "assistant", |
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"content": "", |
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"tool_calls": [ |
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{ |
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"id": response.choices[0].message.tool_calls[0].id, |
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"type": "function", |
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"function": { |
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"name": response.choices[0].message.tool_calls[0].function.name, |
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"arguments": response.choices[0].message.tool_calls[0].function.arguments |
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} |
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}, |
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{ |
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"id": response.choices[0].message.tool_calls[1].id, |
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"type": "function", |
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"function": { |
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"name": response.choices[0].message.tool_calls[1].function.name, |
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"arguments": response.choices[0].message.tool_calls[1].function.arguments |
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} |
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} |
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] |
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}, |
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{ |
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"role": "tool", |
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"content": search(**json.loads(response.choices[0].message.tool_calls[0].function.arguments)), |
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"tool_call_id": response.choices[0].message.tool_calls[0].id # tool_call_id 必須要帶，才能正確配對 工具 及 tool_call |
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}, |
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{ |
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"role": "tool", |
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"content": get_weather(**json.loads(response.choices[0].message.tool_calls[1].function.arguments)), |
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"tool_call_id": response.choices[0].message.tool_calls[1].id # tool_call_id 必須要帶，才能正確配對 工具 及 tool_call |
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} |
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], |
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max_tokens=1500, |
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temperature=0.6, |
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top_p=0.95, |
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tools=tools, |
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tool_choice="auto" |
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) |
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print(response.choices[0].message.reasoning_content) |
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print(response.choices[0].message.content) |
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``` |
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#### 🧠 推理內容輸出（僅顯示部分） |
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> 首先，我需要處理使用者的查詢，他們要求了解臺北市的當下氣溫以及川普最近的關稅政策… |
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> 在呼叫了 `get_weather` 後得到了臺北市氣溫為 26 度（攝氏）… |
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> 接著，使用 `search` 搜尋「川普最新關稅政策 2025」… |
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> 整合後，我提供如下摘要： |
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#### 📋 最終輸出內容 |
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```text |
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以下是您請求的資訊： |
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**臺北市氣溫** |
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- 目前的氣溫為 **26°C**（攝氏） |
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- 天候狀況：晴朗無風 |
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**川普最新關稅政策概述** |
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1. **對等關稅政策** |
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- 對 18 個經濟體課徵 50% 的對等關稅 |
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- 自 4 月 5 日起，所有進口產品全面徵收 10% 基本關稅 |
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2. **報復型對等關稅** |
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- 日本 24%、歐盟 20% |
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3. **對中國的高額關稅** |
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- 增加至 54%（原有關稅 + 新增 34%） |
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4. **特殊案例** |
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- 加拿大與墨西哥不適用，但其他商品課徵 25% |
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- 汽車與部分商品的免稅即將到期 |
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5. **對台灣的影響** |
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- 美國計畫對台灣課徵 32% 關稅，但晶片暫無額外課稅 |
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6. **全球視角** |
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- 歐盟與日本關稅比例相對較高 |
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``` |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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```yaml |
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@misc{twinkleai2025llama3.2f1, |
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title = {Llama-3.2-3B-F1-Reasoning-Instruct: A Traditional Chinese Instruction-Tuned Reasoning Language Model for Taiwan}, |
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author = {Huang, Liang Hsun and Chen, Min Yi and Lin, Wen Bin and Chuang, Chao Chun and Sung, Dave}, |
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year = {2025}, |
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howpublished = {\url{https://huggingface.co/twinkle-ai/Llama-3.2-3B-F1-Instruct}}, |
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note = {Twinkle AI and APMIC. All authors contributed equally.} |
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} |
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``` |
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## Acknowledge |
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- 特此感謝[國家高速網路與計算中心](https://www.nchc.org.tw/)的指導與 [APMIC](https://www.apmic.ai/) 的算力支援，才得以讓本專案訓利完成。 |
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- 特此致謝黃啟聖老師、許武龍（哈爸）、臺北市立第一女子高級中學物理科陳姿燁老師、[奈視科技](https://nanoseex.com/) CTO Howard、[AIPLUX Technology](https://aiplux.com/)、郭家嘉老師以及所有在資料集製作過程中提供寶貴協助的夥伴。 |
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## Model Card Authors |
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[Twinkle AI](https://huggingface.co/twinkle-ai) |
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## Model Card Contact |
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[Twinkle AI](https://huggingface.co/twinkle-ai) |
