--- 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 --- # Llama-3.2-3B-F1-Reasoning-Instruct GGUF Models ## Model Generation Details This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`064cc596`](https://github.com/ggerganov/llama.cpp/commit/064cc596ac44308dc326a17c9e3163c34a6f29d1). ## Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit) 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. # 🚀 If you find these models useful ❤ **Please click "Like" if you find this useful!** Help me test my **AI-Powered Network Monitor Assistant** with **quantum-ready security checks**: 👉 [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__)
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/618dc56cbc345ca7bf95f3cd/lBonfNs_7lzYguD4kJo6z.png) **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 - **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 - **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 ```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)