README.md

---
license: llama3.2
language:
- en
- zh
base_model:
- meta-llama/Llama-3.2-3B
- lianghsun/Llama-3.2-3B-F1-Base
library_name: transformers
tags:
- Taiwan
- R.O.C
- zhtw
- SLM
- Llama-32
datasets:
- lianghsun/tw-reasoning-instruct
- minyichen/tw-instruct-R1-200k
- minyichen/tw_mm_R1
model-index:
- name: Llama-3.2-3B-F1-Reasoning-Instruct
  results:
  - task:
      type: question-answering
      name: Single Choice Question
    dataset:
      type: ikala/tmmluplus
      name: tmmlu+
      config: all
      split: test
      revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c
    metrics:
    - name: single choice
      type: accuracy
      value: 46.16
  - task:
      type: question-answering
      name: Single Choice Question
    dataset:
      type: cais/mmlu
      name: mmlu
      config: all
      split: test
      revision: c30699e
    metrics:
    - name: single choice
      type: accuracy
      value: 51.22
  - task:
      type: question-answering
      name: Single Choice Question
    dataset:
      type: lianghsun/tw-legal-benchmark-v1
      name: tw-legal-benchmark-v1
      config: all
      split: test
      revision: 66c3a5f
    metrics:
    - name: single choice
      type: accuracy
      value: 34.92
metrics:
- accuracy
---

# <span style="color: #7FFF7F;">Llama-3.2-3B-F1-Reasoning-Instruct GGUF Models</span>


## <span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`064cc596`](https://github.com/ggerganov/llama.cpp/commit/064cc596ac44308dc326a17c9e3163c34a6f29d1).




## <span style="color: #7FFF7F;">Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)</span>

Our latest quantization method introduces **precision-adaptive quantization** for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on **Llama-3-8B**. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.

### **Benchmark Context**
All tests conducted on **Llama-3-8B-Instruct** using:
- Standard perplexity evaluation pipeline
- 2048-token context window
- Same prompt set across all quantizations

### **Method**
- **Dynamic Precision Allocation**:  
  - First/Last 25% of layers → IQ4_XS (selected layers)  
  - Middle 50% → IQ2_XXS/IQ3_S (increase efficiency)  
- **Critical Component Protection**:  
  - Embeddings/output layers use Q5_K  
  - Reduces error propagation by 38% vs standard 1-2bit  

### **Quantization Performance Comparison (Llama-3-8B)**

| Quantization | Standard PPL | DynamicGate PPL | Δ PPL   | Std Size | DG Size | Δ Size | Std Speed | DG Speed |
|--------------|--------------|------------------|---------|----------|---------|--------|-----------|----------|
| IQ2_XXS      | 11.30        | 9.84             | -12.9%  | 2.5G     | 2.6G    | +0.1G  | 234s      | 246s     |
| IQ2_XS       | 11.72        | 11.63            | -0.8%   | 2.7G     | 2.8G    | +0.1G  | 242s      | 246s     |
| IQ2_S        | 14.31        | 9.02             | -36.9%  | 2.7G     | 2.9G    | +0.2G  | 238s      | 244s     |
| IQ1_M        | 27.46        | 15.41            | -43.9%  | 2.2G     | 2.5G    | +0.3G  | 206s      | 212s     |
| IQ1_S        | 53.07        | 32.00            | -39.7%  | 2.1G     | 2.4G    | +0.3G  | 184s      | 209s     |

**Key**:
- PPL = Perplexity (lower is better)
- Δ PPL = Percentage change from standard to DynamicGate
- Speed = Inference time (CPU avx2, 2048 token context)
- Size differences reflect mixed quantization overhead

**Key Improvements:**
- 🔥 **IQ1_M** shows massive 43.9% perplexity reduction (27.46 → 15.41)
- 🚀 **IQ2_S** cuts perplexity by 36.9% while adding only 0.2GB
- ⚡ **IQ1_S** maintains 39.7% better accuracy despite 1-bit quantization

**Tradeoffs:**
- All variants have modest size increases (0.1-0.3GB)
- Inference speeds remain comparable (<5% difference)


### **When to Use These Models**
📌 **Fitting models into GPU VRAM**

✔ **Memory-constrained deployments**

✔ **Cpu and Edge Devices** where 1-2bit errors can be tolerated 
 
✔ **Research** into ultra-low-bit quantization



## **Choosing the Right Model Format**  

Selecting the correct model format depends on your **hardware capabilities** and **memory constraints**.  

### **BF16 (Brain Float 16) – Use if BF16 acceleration is available**  
- A 16-bit floating-point format designed for **faster computation** while retaining good precision.  
- Provides **similar dynamic range** as FP32 but with **lower memory usage**.  
- Recommended if your hardware supports **BF16 acceleration** (check your device's specs).  
- Ideal for **high-performance inference** with **reduced memory footprint** compared to FP32.  

📌 **Use BF16 if:**  
✔ Your hardware has native **BF16 support** (e.g., newer GPUs, TPUs).  
✔ You want **higher precision** while saving memory.  
✔ You plan to **requantize** the model into another format.  

📌 **Avoid BF16 if:**  
❌ Your hardware does **not** support BF16 (it may fall back to FP32 and run slower).  
❌ You need compatibility with older devices that lack BF16 optimization.  

---

### **F16 (Float 16) – More widely supported than BF16**  
- A 16-bit floating-point **high precision** but with less of range of values than BF16. 
- Works on most devices with **FP16 acceleration support** (including many GPUs and some CPUs).  
- Slightly lower numerical precision than BF16 but generally sufficient for inference.  

📌 **Use F16 if:**  
✔ Your hardware supports **FP16** but **not BF16**.  
✔ You need a **balance between speed, memory usage, and accuracy**.  
✔ You are running on a **GPU** or another device optimized for FP16 computations.  

📌 **Avoid F16 if:**  
❌ Your device lacks **native FP16 support** (it may run slower than expected).  
❌ You have memory limitations.  

---

### **Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference**  
Quantization reduces model size and memory usage while maintaining as much accuracy as possible.  
- **Lower-bit models (Q4_K)** → **Best for minimal memory usage**, may have lower precision.  
- **Higher-bit models (Q6_K, Q8_0)** → **Better accuracy**, requires more memory.  

📌 **Use Quantized Models if:**  
✔ You are running inference on a **CPU** and need an optimized model.  
✔ Your device has **low VRAM** and cannot load full-precision models.  
✔ You want to reduce **memory footprint** while keeping reasonable accuracy.  

📌 **Avoid Quantized Models if:**  
❌ You need **maximum accuracy** (full-precision models are better for this).  
❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).  

---

### **Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)**  
These models are optimized for **extreme memory efficiency**, making them ideal for **low-power devices** or **large-scale deployments** where memory is a critical constraint.  

- **IQ3_XS**: Ultra-low-bit quantization (3-bit) with **extreme memory efficiency**.  
  - **Use case**: Best for **ultra-low-memory devices** where even Q4_K is too large.  
  - **Trade-off**: Lower accuracy compared to higher-bit quantizations.  

- **IQ3_S**: Small block size for **maximum memory efficiency**.  
  - **Use case**: Best for **low-memory devices** where **IQ3_XS** is too aggressive.  

- **IQ3_M**: Medium block size for better accuracy than **IQ3_S**.  
  - **Use case**: Suitable for **low-memory devices** where **IQ3_S** is too limiting.  

- **Q4_K**: 4-bit quantization with **block-wise optimization** for better accuracy.  
  - **Use case**: Best for **low-memory devices** where **Q6_K** is too large.  

- **Q4_0**: Pure 4-bit quantization, optimized for **ARM devices**.  
  - **Use case**: Best for **ARM-based devices** or **low-memory environments**.  

---

### **Summary Table: Model Format Selection**  

| Model Format  | Precision  | Memory Usage  | Device Requirements  | Best Use Case  |  
|--------------|------------|---------------|----------------------|---------------|  
| **BF16**     | Highest    | High          | BF16-supported GPU/CPUs  | High-speed inference with reduced memory |  
| **F16**      | High       | High          | FP16-supported devices | GPU inference when BF16 isn't available |  
| **Q4_K**     | Medium Low | Low           | CPU or Low-VRAM devices | Best for memory-constrained environments |  
| **Q6_K**     | Medium     | Moderate      | CPU with more memory | Better accuracy while still being quantized |  
| **Q8_0**     | High       | Moderate      | CPU or GPU with enough VRAM | Best accuracy among quantized models |  
| **IQ3_XS**   | Very Low   | Very Low      | Ultra-low-memory devices | Extreme memory efficiency and low accuracy |  
| **Q4_0**     | Low        | Low           | ARM or low-memory devices | llama.cpp can optimize for ARM devices |  

---

## **Included Files & Details**  

### `Llama-3.2-3B-F1-Reasoning-Instruct-bf16.gguf`  
- Model weights preserved in **BF16**.  
- Use this if you want to **requantize** the model into a different format.  
- Best if your device supports **BF16 acceleration**.  

### `Llama-3.2-3B-F1-Reasoning-Instruct-f16.gguf`  
- Model weights stored in **F16**.  
- Use if your device supports **FP16**, especially if BF16 is not available.  

### `Llama-3.2-3B-F1-Reasoning-Instruct-bf16-q8_0.gguf`  
- **Output & embeddings** remain in **BF16**.  
- All other layers quantized to **Q8_0**.  
- Use if your device supports **BF16** and you want a quantized version.  

### `Llama-3.2-3B-F1-Reasoning-Instruct-f16-q8_0.gguf`  
- **Output & embeddings** remain in **F16**.  
- All other layers quantized to **Q8_0**.    

### `Llama-3.2-3B-F1-Reasoning-Instruct-q4_k.gguf`  
- **Output & embeddings** quantized to **Q8_0**.  
- All other layers quantized to **Q4_K**.  
- Good for **CPU inference** with limited memory.  

### `Llama-3.2-3B-F1-Reasoning-Instruct-q4_k_s.gguf`  
- Smallest **Q4_K** variant, using less memory at the cost of accuracy.  
- Best for **very low-memory setups**.  

### `Llama-3.2-3B-F1-Reasoning-Instruct-q6_k.gguf`  
- **Output & embeddings** quantized to **Q8_0**.  
- All other layers quantized to **Q6_K** .  

### `Llama-3.2-3B-F1-Reasoning-Instruct-q8_0.gguf`  
- Fully **Q8** quantized model for better accuracy.  
- Requires **more memory** but offers higher precision.  

### `Llama-3.2-3B-F1-Reasoning-Instruct-iq3_xs.gguf`  
- **IQ3_XS** quantization, optimized for **extreme memory efficiency**.  
- Best for **ultra-low-memory devices**.  

### `Llama-3.2-3B-F1-Reasoning-Instruct-iq3_m.gguf`  
- **IQ3_M** quantization, offering a **medium block size** for better accuracy.  
- Suitable for **low-memory devices**.  

### `Llama-3.2-3B-F1-Reasoning-Instruct-q4_0.gguf`  
- Pure **Q4_0** quantization, optimized for **ARM devices**.  
- Best for **low-memory environments**.
- Prefer IQ4_NL for better accuracy.

# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>
❤ **Please click "Like" if you find this useful!**  
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👉 [Free Network Monitor](https://readyforquantum.com/dashboard/?assistant=open)  

💬 **How to test**:  
 Choose an **AI assistant type**:  
   - `TurboLLM` (GPT-4o-mini)  
   - `HugLLM` (Hugginface Open-source)  
   - `TestLLM` (Experimental CPU-only)  

### **What I’m Testing**  
I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:  
- **Function calling** against live network services  
- **How small can a model go** while still handling:  
  - Automated **Nmap scans**  
  - **Quantum-readiness checks**  
  - **Network Monitoring tasks**  

🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads):  
- ✅ **Zero-configuration setup**  
- ⏳ 30s load time (slow inference but **no API costs**)  
- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!  

### **Other Assistants**  
🟢 **TurboLLM** – Uses **gpt-4o-mini** for: 
- **Create custom cmd processors to run .net code on Free Network Monitor Agents**
- **Real-time network diagnostics and monitoring**
- **Security Audits**
- **Penetration testing** (Nmap/Metasploit)  
- 🔑 Get more tokens by logging in or [downloading our Free Network Monitor Agent with integrated AI Assistant](https://readyforquantum.com/download)  

🔵 **HugLLM** – Latest Open-source models:  
- 🌐 Runs on Hugging Face Inference API  

### 💡 **Example commands to you could test**:  
1. `"Give me info on my websites SSL certificate"`  
2. `"Check if my server is using quantum safe encyption for communication"`  
3. `"Run a comprehensive security audit on my server"`
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Free Network Monitor Agent to run the .net code from. This is a very flexible and powerful feature. Use with caution!



# Model Card for Llama-3.2-3B-F1-Reasoning-Instruct (a.k.a __Formosa-1-Reasoning__ or __F1-Reasoning__)

<div align="center" style="line-height: 1;">
  <a href="https://discord.gg/Cx737yw4ed" target="_blank" style="margin: 2px;">
    <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;"/>
  </a>
  <a href="https://huggingface.co/twinkle-ai" target="_blank" style="margin: 2px;">
    <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;"/>
  </a>
</div>

<div align="center" style="line-height: 1;">
  <a href="https://huggingface.co/meta-llama/Llama-3.2-1B/blob/main/LICENSE.txt" style="margin: 2px;">
    <img alt="License" src="https://img.shields.io/badge/License-llama3.2-f5de53?&color=0081fb" style="display: inline-block; vertical-align: middle;"/>
  </a>
</div>

![image/png](https://cdn-uploads.huggingface.co/production/uploads/618dc56cbc345ca7bf95f3cd/lBonfNs_7lzYguD4kJo6z.png)

<!-- Provide a quick summary of what the model is/does. -->
**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。

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **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.)
- **Funded by:** [APMIC](https://www.apmic.ai/)
- **Model type:** LlamaForCausalLM
- **Language(s) (NLP):** Tranditional Chinese & English
- **License:** [llama3.2](https://huggingface.co/meta-llama/Llama-3.2-1B/blob/main/LICENSE.txt)

### Model Sources
<!-- Provide the basic links for the model. -->

- **Repository:** [twinkle-ai/Llama-3.2-3B-F1-Reasoning-Instruct](https://huggingface.co/twinkle-ai/Llama-3.2-3B-F1-Reasoning-Instruct)
- **Paper:** (TBA)
- **Demo:** [Playground](https://3b02.coolify.apmic.ai/)

## Evaluation

### Results

下表採用 [🌟 Twinkle Eval](https://github.com/ai-twinkle/Eval) 評測框架
| 模型                               | 評測模式 | TMMLU+(%)       | 台灣法律(%)      | MMLU(%)         | 測試次數 | 選項排序 |
|------------------------------------|---------|----------------|----------------|----------------|---------|---------|
| [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    |
| [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    |
| [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    |
| [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    |
| [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    |

下表用 lighteval 評測框架
| 模型                                       | MATH-500 | GPQA Diamond |
|--------------------------------------------|----------|--------------|
| [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)                       | 44.40    | 27.78        |
| [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**    |


---

## 🔧 Tool Calling

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。
Tool call 模板已經為大家寫好放進 chat-template 了，Enjoy it！

### 1️⃣ 啟動 vLLM 後端
> **⚠️ 注意：需要 vLLM 版本 >= 0.8.3，否則 `enable-reasoning`、`enable-auto-tool-choice` 無法同時開啟**

```bash
vllm serve twinkle-ai/Llama-3.2-3B-F1-Reasoning-Instruct \
  --port 8001 \
  --enable-reasoning \
  --reasoning-parser deepseek_r1 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes
```

### 2️⃣ 定義工具（Functions）

```python
def get_weather(location: str, unit: str):
    return f"{location}的氣溫是{unit}26度，晴朗無風"

def search(query: str):
    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。"

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string", "description": "國家或城市名, e.g., 'Taipei'、'Jaipei'"},
                    "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"]}
                },
                "required": ["location", "unit"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "search",
            "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。",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "should be a search query, e.g., '2024 南韓 戒嚴'"}
                },
                "required": ["query"]
            }
        }
    }
]
```

### 3️⃣ 執行工具調用（Tool Calls）

> **⚠️ 注意：system_prompt 可以不用帶，除非是需要時間基準的工具。**
```python
response = client.chat.completions.create(
    model=client.models.list().data[0].id,
    messages=[
        {"role": "system", "content": "記住你的知識截止於 2024/12，今天是 2025/4/7"},
        {"role": "user", "content": "台北氣溫如何? 另外，告訴我川普最新關稅政策"},
    ],
    max_tokens=1500,
    temperature=0.6,
    top_p=0.95,
    tools=tools,
    tool_choice="auto"
)

print(response.choices[0].message.reasoning_content)
print(response.choices[0].message.tool_calls)
```

#### 🧠 推理內容輸出（僅顯示部分）
> 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。  
> 對於第一部分，他們提到了“臺北”，所以應該呼叫 get_weather 函式…  
> 接下來是關於川普的新關稅政策…  
> 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…

#### ⚙️ Tool Calls List


```json
[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')]
```

### 4️⃣ 產生最終回答

```python
response = client.chat.completions.create(
    model=client.models.list().data[0].id,
    messages=[
        {"role": "system", "content": "記住你的知識截止於 2024/12，今天是 2025/4/7"},
        {"role": "user", "content": "台北氣溫如何? 另外，告訴我川普最新關稅政策"},
        {
            "role": "assistant",
            "content": "",
            "tool_calls": [
                {
                    "id": response.choices[0].message.tool_calls[0].id,
                    "type": "function",
                    "function": {
                        "name": response.choices[0].message.tool_calls[0].function.name,
                        "arguments": response.choices[0].message.tool_calls[0].function.arguments
                    }
                },
                {
                    "id": response.choices[0].message.tool_calls[1].id,
                    "type": "function",
                    "function": {
                        "name": response.choices[0].message.tool_calls[1].function.name,
                        "arguments": response.choices[0].message.tool_calls[1].function.arguments
                    }
                }
            ]
        },
        {
            "role": "tool",
            "content": search(**json.loads(response.choices[0].message.tool_calls[0].function.arguments)),
            "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
        },
        {
            "role": "tool",
            "content": get_weather(**json.loads(response.choices[0].message.tool_calls[1].function.arguments)),
            "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
        }
    ],
    max_tokens=1500,
    temperature=0.6,
    top_p=0.95,
    tools=tools,
    tool_choice="auto"
)

print(response.choices[0].message.reasoning_content)
print(response.choices[0].message.content)
```

#### 🧠 推理內容輸出（僅顯示部分）
> 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…  
> 在呼叫了 `get_weather` 後得到了臺北市氣溫為 26 度（攝氏）…  
> 接著，使用 `search` 搜尋「川普最新關稅政策 2025」…  
> 整合後，我提供如下摘要：

#### 📋 最終輸出內容

```text
以下是您請求的資訊：

**臺北市氣溫**
- 目前的氣溫為 **26°C**（攝氏）
- 天候狀況：晴朗無風

**川普最新關稅政策概述**
1. **對等關稅政策**  
   - 對 18 個經濟體課徵 50% 的對等關稅  
   - 自 4 月 5 日起，所有進口產品全面徵收 10% 基本關稅  

2. **報復型對等關稅**  
   - 日本 24%、歐盟 20%  

3. **對中國的高額關稅**  
   - 增加至 54%（原有關稅 + 新增 34%）  

4. **特殊案例**  
   - 加拿大與墨西哥不適用，但其他商品課徵 25%  
   - 汽車與部分商品的免稅即將到期  

5. **對台灣的影響**  
   - 美國計畫對台灣課徵 32% 關稅，但晶片暫無額外課稅  

6. **全球視角**  
   - 歐盟與日本關稅比例相對較高
```


## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
```yaml
@misc{twinkleai2025llama3.2f1,
  title        = {Llama-3.2-3B-F1-Reasoning-Instruct: A Traditional Chinese Instruction-Tuned Reasoning Language Model for Taiwan},
  author       = {Huang, Liang Hsun and Chen, Min Yi and Lin, Wen Bin and Chuang, Chao Chun and Sung, Dave},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/twinkle-ai/Llama-3.2-3B-F1-Instruct}},
  note         = {Twinkle AI and APMIC. All authors contributed equally.}
}
```

## Acknowledge
- 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。
- 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。

## Model Card Authors

[Twinkle AI](https://huggingface.co/twinkle-ai)

## Model Card Contact

[Twinkle AI](https://huggingface.co/twinkle-ai)