From 3046e1d40b56b064f25d600bfe6523fed027575b Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Wed, 17 Jun 2026 04:50:12 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: twinkle-ai/Llama-3.2-3B-F1-Instruct Source: Original Platform --- .gitattributes | 49 + README.md | 339 +++++ config.json | 40 + configuration.json | 1 + generation_config.json | 12 + model-00001-of-00002.safetensors | 3 + model-00002-of-00002.safetensors | 3 + model.safetensors.index.json | 262 ++++ special_tokens_map.json | 27 + tokenizer.json | 3 + tokenizer_config.json | 2070 ++++++++++++++++++++++++++++++ 11 files changed, 2809 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 config.json create mode 100644 configuration.json create mode 100644 generation_config.json create mode 100644 model-00001-of-00002.safetensors create mode 100644 model-00002-of-00002.safetensors create mode 100644 model.safetensors.index.json create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..21b3632 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,49 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +tokenizer.json filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..d8ee314 --- /dev/null +++ b/README.md @@ -0,0 +1,339 @@ +--- +license: llama3.2 +language: +- en +- zh +base_model: +- meta-llama/Llama-3.2-3B +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-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: 44.11 + - 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: 50.64 + - 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: 35.24 +metrics: +- accuracy +--- + +# Model Card for Llama-3.2-3B-F1-Instruct (a.k.a __Formosa-1__ or __F1__) + +
+ + Discord + + + Hugging Face + +
+ +
+ + License + +
+ +![image/png](https://cdn-uploads.huggingface.co/production/uploads/618dc56cbc345ca7bf95f3cd/lBonfNs_7lzYguD4kJo6z.png) + + + +**Llama-3.2-3B-F1-Instruct**（a.k.a **Formosa-1** or **F1**） 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-Instruct](https://huggingface.co/twinkle-ai/Llama-3.2-3B-F1-Instruct) +- **Paper:** (TBA) + +## 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-Instruct](https://huggingface.co/twinkle-ai/Llama-3.2-3B-F1-Instruct) (ours) | box | 44.11 (±0.0179) | 35.24 (±0.0119) | 50.64 (±0.0189) | 3 | 隨機 | + + + +### Function Calling Benchmark + +我們採用了 **BFCL (Berkeley Function Calling Leaderboard)** 來評估模型在 Function Calling（函式呼叫）任務中的表現。 + +測試使用的指標如下： + +- **AST Accuracy（AST 正確率）**： + 比較模型生成的函式呼叫與目標答案在抽象語法樹（AST）上的結構相似度。涵蓋四種題型： + - 單一函式（Simple Function） + - 多函式（Multiple Function） + - 平行函式（Parallel Function） + - 平行多函式（Parallel Multiple Function） + + +| Model | Overall Accuracy | AST Accuracy (S.) | AST Accuracy (M.) | AST Accuracy (P.) | AST Accuracy (P.M.) | +|-----------------|------------------|-------------------|-------------------|-------------------|---------------------| +| [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) | 84 | 92 | 92 | 80 | 74 | +| [MediaTek-Research/Llama-Breeze2-3B-Instruct](https://huggingface.co/MediaTek-Research/Llama-Breeze2-3B-Instruct) | 85 | 92 | 92 | 84 | 81 | +| [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) | 57 | 56 | 54 | 49 | 35 | +| [MediaTek-Research/Llama-Breeze2-8B-Instruct](https://huggingface.co/MediaTek-Research/Llama-Breeze2-8B-Instruct) | 87 | 91 | 93 | 86 | 81 | +| GPT-4o-mini(2024-07-18) | 87 | 91 | 93 | 90 | 84 | +| 🌟[twinkle-ai/Llama-3.2-3B-F1-Instruct](https://huggingface.co/twinkle-ai/Llama-3.2-3B-F1-Instruct) (ours) | **91** | 93 | 95 | 91 | 87 | + +_**Note:** 部分數據取自 [Breeze 的論文](https://arxiv.org/pdf/2501.13921)。_ + +--- + +## 🔧 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 + +```bash +vllm serve twinkle-ai/Llama-3.2-3B-F1-Instruct \ + --port 8001 \ + --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", + extra_body={"skip_special_tokens": False} +) + +print(response.choices[0].message.tool_calls) +``` + +#### ⚙️ 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", + extra_body={"skip_special_tokens": False} +) + +print(response.choices[0].message.content) +``` + +#### 📋 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-Instruct: A Traditional Chinese Instruction-Tuned 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) diff --git a/config.json b/config.json new file mode 100644 index 0000000..93e133d --- /dev/null +++ b/config.json @@ -0,0 +1,40 @@ +{ + "_name_or_path": "meta-llama/Llama-3.2-3B", + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": [ + 128001, + 128008, + 128009 + ], + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 3072, + "initializer_range": 0.02, + "intermediate_size": 8192, + "max_position_embeddings": 131072, + 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"additional_special_tokens": [ + "<|eot_id|>", + "<|eom_id|>" + ], + "bos_token": "<|begin_of_text|>", + "chat_template": "{%- macro json_to_python_type(json_spec) %}\n {%- set basic_type_map = {\n \"string\": \"str\",\n \"number\": \"float\",\n \"integer\": \"int\",\n \"boolean\": \"bool\"\n } %}\n {%- if basic_type_map[json_spec.type] is defined %}\n {{- basic_type_map[json_spec.type] }}\n {%- elif json_spec.type == \"array\" %}\n {{- \"list[\" + json_to_python_type(json_spec|items) + \"]\" }}\n {%- elif json_spec.type == \"object\" %}\n {%- if json_spec.additionalProperties is defined %}\n {{- \"dict[str, \" + json_to_python_type(json_spec.additionalProperties) + \"]\" }}\n {%- else %}\n {{- \"dict\" }}\n {%- endif %}\n {%- elif json_spec.type is iterable %}\n {{- \"Union[\" }}\n {%- for t in json_spec.type %}\n {{- json_to_python_type({\"type\": t}) }}{{ \", \" if not loop.last }}\n {%- endfor %}\n {{- \"]\" }}\n {%- else %}\n {{- \"Any\" }}\n {%- endif %}\n{%- endmacro %}\n\n{%- if tools and tools|length > 0 %}\n{{- bos_token }}<|start_header_id|>system<|end_header_id|>\n\n{{ (messages | selectattr(\"role\", \"equalto\", \"system\") | list | first).content if (messages | selectattr(\"role\", \"equalto\", \"system\") | list | length > 0) else \"You are a function calling AI model.\" }} You are provided with function signatures within XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools:\n\n\n[\n{%- for tool in tools -%}\n {%- set func = tool.get('function', tool) -%}\n {\"type\": \"function\", \"function\": {\"name\": \"{{ func.name }}\", \"description\": \"{{ func.description }}\", \"parameters\": {{ func.parameters | tojson }}}}{{ \",\" if not loop.last }}\n{%- endfor -%}\n]\n\n\nFor each function call return a json object with function name and arguments within tags with the following schema:\n\n{\"arguments\": , \"name\": }\n<|eot_id|>\n\n{%- set processed_messages = messages | rejectattr(\"role\", \"equalto\", \"system\") | list %}\n{%- set ns = namespace(pairs=[]) %}\n{%- for message in processed_messages if message.role == 'assistant' and message.tool_calls is defined %}\n {%- for tc in message.tool_calls %}\n {%- set ns.pairs = ns.pairs + [(tc.id, tc.function.name)] %}\n {%- endfor %}\n{%- endfor %}\n{%- set tool_call_map = dict(ns.pairs) %}\n\n{%- for message in processed_messages %}\n {%- set is_first_tool = message.role == 'tool' and (loop.index0 == 0 or processed_messages[loop.index0-1].role != 'tool') %}\n {%- set is_last_tool = message.role == 'tool' and (loop.last or processed_messages[loop.index0+1].role != 'tool') %}\n \n {%- if message.role == \"user\" %}\n<|start_header_id|>user<|end_header_id|>\n\n{{ message.content }}<|eot_id|>\n {%- elif message.role == \"assistant\" %}\n<|start_header_id|>assistant<|end_header_id|>\n\n {% if message.tool_calls is defined %}\n{%- for tool_call in message.tool_calls %}\n\n{\"name\": \"{{ tool_call.function.name }}\", \"arguments\": {{ tool_call.function.arguments | tojson }}}\n{% if not loop.last %}\n\n{% endif %}\n{%- endfor %}\n {%- else %}\n{{ message.content }}\n {%- endif %}\n<|eot_id|>\n {%- elif message.role == \"tool\" %}\n {%- if is_first_tool %}\n<|start_header_id|>ipython<|end_header_id|>\n\n {% endif %}\n\n{{ {\"name\": tool_call_map.get(message.tool_call_id, \"unknown\"), \"content\": message.content} | tojson }}\n\n {%- if not is_last_tool %}\n\n {% else %}\n<|eot_id|>\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n\n{%- if add_generation_prompt %}\n<|start_header_id|>assistant<|end_header_id|>\n\n{% endif %}\n\n{%- else %}\n{%- set loop_messages = messages %}\n{%- for message in loop_messages %}\n {%- set content = '<|start_header_id|>' ~ message['role'] ~ '<|end_header_id|>\\n\\n' ~ (message['content']|trim) ~ '<|eot_id|>' %}\n {%- if loop.index0 == 0 %}{%- set content = bos_token ~ content %}{%- endif %}\n{{ content }}\n{%- endfor %}\n{%- if add_generation_prompt %}\n{{ '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n{%- endif %}\n", + "clean_up_tokenization_spaces": true, + "eos_token": "<|end_of_text|>", + "extra_special_tokens": {}, + "model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 65536, + "pad_token": "<|end_of_text|>", + "padding_side": "right", + "split_special_tokens": false, + "tokenizer_class": "PreTrainedTokenizerFast" +}