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---
license: Apache License 2.0
#model-type:
##如 gpt、phi、llama、chatglm、baichuan 等
#- gpt
#domain:
##如 nlp、cv、audio、multi-modal
#- nlp
#language:
##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
#- cn
#metrics:
##如 CIDEr、Blue、ROUGE 等
#- CIDEr
#tags:
##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
#- pretrained
#tools:
##如 vllm、fastchat、llamacpp、AdaSeq 等
#- vllm
tags:
- unsloth
base_model:
- HuggingFaceTB/SmolLM3-3B
library_name: transformers
license: apache-2.0
language:
- en
- fr
- es
- it
- pt
- zh
- ar
- ru
---
### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
<div>
<p style="margin-top: 0;margin-bottom: 0;">
<em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
</p>
<div style="display: flex; gap: 5px; align-items: center; ">
<a href="https://github.com/unslothai/unsloth/">
<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
</a>
<a href="https://discord.gg/unsloth">
<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
</a>
<a href="https://docs.unsloth.ai/">
<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
</a>
</div>
</div>
SDK下载
# SmolLM3
![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/zy0dqTCCt5IHmuzwoqtJ9.png)
## Table of Contents
1. [Model Summary](#model-summary)
2. [How to use](#how-to-use)
3. [Evaluation](#evaluation)
4. [Training](#training)
5. [Limitations](#limitations)
6. [License](#license)
## Model Summary
SmolLM3 is a 3B parameter language model designed to push the boundaries of small models. It supports 6 languages, advanced reasoning and long context. SmolLM3 is a fully open model that offers strong performance at the 3B–4B scale.
![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/db3az7eGzs-Sb-8yUj-ff.png)
The model is a decoder-only transformer using GQA and NoPE (with 3:1 ratio), it was pretrained on 11.2T tokens with a staged curriculum of web, code, math and reasoning data. Post-training included midtraining on 140B reasoning tokens followed by supervised fine-tuning and alignment via Anchored Preference Optimization (APO).
### Key features
- Instruct model optimized for **hybrid reasoning**
- **Fully open model**: open weights + full training details including public data mixture and training configs
- **Long context:** Trained on 64k context and suppots up to **128k tokens** using YARN extrapolation
- **Multilingual**: 6 natively supported (English, French, Spanish, German, Italian, and Portuguese)
For more details refer to our blog post: https://hf.co/blog/smollm3
## How to use
The modeling code for SmolLM3 is available in transformers `v4.53.0`, so make sure to upgrade your transformers version. You can also load the model with the latest `vllm` which uses transformers as a backend.
```bash
#安装ModelScope
pip install modelscope
```
```python
#SDK模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('unsloth/SmolLM3-3B')
```
Git下载
```
#Git模型下载
git clone https://www.modelscope.cn/unsloth/SmolLM3-3B.git
pip install -U transformers
```
<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "HuggingFaceTB/SmolLM3-3B"
device = "cuda" # for GPU usage or "cpu" for CPU usage
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
).to(device)
# prepare the model input
prompt = "Give me a brief explanation of gravity in simple terms."
messages_think = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages_think,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate the output
generated_ids = model.generate(**model_inputs, max_new_tokens=32768)
# Get and decode the output
output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :]
print(tokenizer.decode(output_ids, skip_special_tokens=True))
```
>[!TIP]
> We recommend setting `temperature=0.6` and `top_p=0.95` in the sampling parameters.
### Enabling and Disabling Extended Thinking Mode
We enable extended thinking by default, so the example above generates the output with a reasoning trace. For choosing between enabling, you can provide the `/think` and `/no_think` flags through the system prompt as shown in the snippet below for extended thinking disabled. The code for generating the response with extended thinking would be the same except that the system prompt should have `/think` instead of `/no_think`.
```python
prompt = "Give me a brief explanation of gravity in simple terms."
messages = [
{"role": "system", "content": "/no_think"},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
```
We also provide the option of specifying the whether to use extended thinking through the `enable_thinking` kwarg as in the example below. You do not need to set the `/no_think` or `/think` flags through the system prompt if using the kwarg, but keep in mind that the flag in the system prompt overwrites the setting in the kwarg.
```python
prompt = "Give me a brief explanation of gravity in simple terms."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False
)
```
### Agentic Usage
SmolLM3 supports tool calling!
Just pass your list of tools:
- Under the argument `xml_tools` for standard tool-calling: these tools will be called as JSON blobs within XML tags, like `<tool_call>{"name": "get_weather", "arguments": {"city": "Copenhagen"}}</tool_call>`
- Or under `python_tools`: then the model will call tools like python functions in a `<code>` snippet, like `<code>get_weather(city="Copenhagen")</code>`
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM3-3B"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)
tools = [
{
"name": "get_weather",
"description": "Get the weather in a city",
"parameters": {"type": "object", "properties": {"city": {"type": "string", "description": "The city to get the weather for"}}}}
]
messages = [
{
"role": "user",
"content": "Hello! How is the weather today in Copenhagen?"
}
]
inputs = tokenizer.apply_chat_template(
messages,
enable_thinking=False, # True works as well, your choice!
xml_tools=tools,
add_generation_prompt=True,
tokenize=True,
return_tensors="pt"
)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
### Using Custom System Instructions.
You can specify custom instruction through the system prompt while controlling whether to use extended thinking. For example, the snippet below shows how to make the model speak like a pirate while enabling extended thinking.
```python
prompt = "Give me a brief explanation of gravity in simple terms."
messages = [
{"role": "system", "content": "Speak like a pirate./think"},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
```
For local inference, you can use `llama.cpp`, `ONNX`, `MLX` and `MLC`. You can find quantized checkpoints in this collection (https://huggingface.co/collections/HuggingFaceTB/smollm3-686d33c1fdffe8e635317e23)
### vLLM and SGLang
You can use vLLM and SGLang to deploy the model in an API compatible with OpenAI format.
#### SGLang
```bash
python -m sglang.launch_server --model-path HuggingFaceTB/SmolLM3-3B
```
#### vLLM
```bash
vllm serve HuggingFaceTB/SmolLM3-3B
```
#### Setting `chat_template_kwargs`
You can specify `chat_template_kwargs` such as `enable_thinking` and `xml_tools` to a deployed model by passing the `chat_template_kwargs` parameter in the API request.
```bash
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "HuggingFaceTB/SmolLM3-3B",
"messages": [
{"role": "user", "content": "Give me a brief explanation of gravity in simple terms."}
],
"temperature": 0.6,
"top_p": 0.95,
"max_tokens": 16384,
"chat_template_kwargs": {"enable_thinking": false}
}'
```
## Evaluation
In this section, we report the evaluation results of SmolLM3 model. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them.
We highlight the best score in bold and underline the second-best score.
### Instruction Model
#### No Extended Thinking
Evaluation results of non reasoning models and reasoning models in no thinking mode. We highlight the best and second-best scores in bold.
| Category | Metric | SmoLLM3-3B | Qwen2.5-3B | Llama3.1-3B | Qwen3-1.7B | Qwen3-4B |
|---------|--------|------------|------------|-------------|------------|----------|
| High school math competition | AIME 2025 | <u>9.3</u> | 2.9 | 0.3 | 8.0 | **17.1** |
| Math problem-solving | GSM-Plus | 72.8 | <u>74.1</u> | 59.2 | 68.3 | **82.1** |
| Competitive programming | LiveCodeBench v4 | <u>15.2</u> | 10.5 | 3.4 | 15.0 | **24.9** |
| Graduate-level reasoning | GPQA Diamond | <u>35.7</u> | 32.2 | 29.4 | 31.8 | **44.4** |
| Instruction following | IFEval | **76.7** | 65.6 | 71.6 | <u>74.0</u> | 68.9 |
| Alignment | MixEval Hard | 26.9 | <u>27.6</u> | 24.9 | 24.3 | **31.6** |
| Tool Calling | BFCL| <u>92.3</u> | - | <u>92.3</u> * | 89.5 | **95.0** |
| Multilingual Q&A | Global MMLU | <u>53.5</u> | 50.54 | 46.8 | 49.5 | **65.1** |
(*): this is a tool calling finetune
#### Extended Thinking
Evaluation results in reasoning mode for SmolLM3 and Qwen3 models:
| Category | Metric | SmoLLM3-3B | Qwen3-1.7B | Qwen3-4B |
|---------|--------|------------|------------|----------|
| High school math competition | AIME 2025 | <u>36.7</u> | 30.7 | **58.8** |
| Math problem-solving | GSM-Plus | <u>83.4</u> | 79.4 | **88.2** |
| Competitive programming | LiveCodeBench v4 | 30.0 | <u>34.4</u> | **52.9** |
| Graduate-level reasoning | GPQA Diamond | <u>41.7</u> | 39.9 | **55.3** |
| Instruction following | IFEval | 71.2 | <u>74.2</u> | **85.4** |
| Alignment | MixEval Hard | 30.8 | <u>33.9</u> | **38.0** |
| Tool Calling | BFCL | <u>88.8</u> | <u>88.8</u> | **95.5** |
| Multilingual Q&A | Global MMLU | <u>64.1</u> | 62.3 | **73.3** |
### Base Pre-Trained Model
#### English benchmarks
Note: All evaluations are zero-shot unless stated otherwise. For Ruler 64k evaluation, we apply YaRN to the Qwen models with 32k context to extrapolate the context length.
| Category | Metric | SmolLM3-3B | Qwen2.5-3B | Llama3-3.2B | Qwen3-1.7B-Base | Qwen3-4B-Base |
|---------|--------|---------------------|------------|--------------|------------------|---------------|
| Reasoning & Commonsense| HellaSwag | **76.15** | 74.19 |<u>75.52</u> | 60.52 | 74.37 |
| | ARC-CF (Average) | **65.61** | 59.81 | 58.58 | 55.88 | <u>62.11</u> |
| | Winogrande | 58.88 | **61.41** | 58.72 | 57.06 | <u>59.59</u> |
| | CommonsenseQA | <u>55.28</u> | 49.14 | **60.60** | 48.98 | 52.99 |
| Knowledge & Understanding | MMLU-CF (Average) | <u>44.13</u> | 42.93 | 41.32 | 39.11 | **47.65** |
| | MMLU Pro CF | <u>19.61</u> | 16.66 | 16.42 | 18.04 | **24.92** |
| | MMLU Pro MCF | <u>32.70</u> | 31.32 | 25.07 | 30.39 | **41.07** |
| | PIQA | **78.89** | 78.35 | <u>78.51</u> | 75.35 | 77.58 |
| | OpenBookQA | 40.60 | 40.20 | <u>42.00</u> | 36.40 | **42.40** |
| | BoolQ | **78.99** | 73.61 | <u>75.33</u> | 74.46 | 74.28 |
| **Math & Code** | | | | | | |
| Coding & math | HumanEval+ | 30.48 | 34.14| 25.00 | <u>43.29</u>| **54.87** |
| | MBPP+ | 52.91 | 52.11 | 38.88| <u>59.25</u> | **63.75** |
| | MATH (4-shot) | <u>46.10</u> | 40.10 | 7.44 | 41.64 | **51.20** |
| | GSM8k (5-shot) | 67.63 | <u>70.13</u> | 25.92 | 65.88 | **74.14** |
| **Long context** | | | | | | |
| | Ruler 32k | 76.35 | 75.93 | <u>77.58</u> | 70.63 | **83.98** |
| | Ruler 64k | <u>67.85</u> | 64.90 | **72.93** | 57.18 | 60.29 |
| | Ruler 128k | 61.03 | <u>62.23</u> | **71.30** | 43.03 | 47.23 |
#### Multilingual benchmarks
| Category | Metric | SmolLM3 3B Base | Qwen2.5-3B | Llama3.2 3B | Qwen3 1.7B Base | Qwen3 4B Base |
|---------|--------|---------------------|------------|--------------|------------------|---------------|
| Main supported languages | | | | | | | |
| French| MLMM Hellaswag | **63.94** | 57.47 | 57.66 | 51.26 | <u>61.00</u> |
| | Belebele | 51.00 | <u>51.55</u> | 49.22 |49.44| **55.00** |
| | Global MMLU (CF) | <u>38.37</u> | 34.22 | 33.71 | 34.94 |**41.80** |
| | Flores-200 (5-shot) | 62.85| 61.38| <u>62.89<u/u> | 58.68 | **65.76** |
| Spanish| MLMM Hellaswag | **65.85** | 58.25 | 59.39 | 52.40 | <u>61.85</u> |
| | Belebele | 47.00 | <u>48.88</u> | 47.00 | 47.56 | **50.33** |
| | Global MMLU (CF) | <u>38.51</u> | 35.84 | 35.60 | 34.79 |**41.22** |
| | Flores-200 (5-shot) | <u>48.25</u>| 50.00| 44.45 | 46.93 | **50.16** |
| German| MLMM Hellaswag | **59.56** | 49.99| 53.19|46.10| <u>56.43</u>|
| | Belebele | <u>48.44</u> | 47.88 | 46.22 | 48.00 | **53.44**|
| | Global MMLU (CF) | <u>35.10</u> | 33.19 | 32.60 | 32.73 |**38.70** |
| | Flores-200 (5-shot) | **56.60**| 50.63| <u>54.95</u> | 52.58 | 50.48 |
| Italian| MLMM Hellaswag | **62.49** | 53.21 | 54.96 | 48.72 | <u>58.76</u> |
| | Belebele | <u>46.44</u> | 44.77 | 43.88 | 44.00 | **48.78** | 44.88 |
| | Global MMLU (CF) | <u>36.99</u> | 33.91 | 32.79 | 35.37 |**39.26** |
| | Flores-200 (5-shot) | <u>52.65<u/>| **54.87**| 48.83 | 48.37 | 49.11 |
| Portuguese| MLMM Hellaswag | **63.22** | 57.38 | 56.84 | 50.73 | <u>59.89</u> |
| | Belebele | 47.67 | **49.22** | 45.00 | 44.00 | 50.00 | <u>49.00</U> |
| | Global MMLU (CF) | <u>36.88</u> | 34.72 | 33.05 | 35.26 |**40.66** |
| | Flores-200 (5-shot) | <u>60.93</u> |57.68| 54.28 | 56.58 | **63.43** |
The model has also been trained on Arabic (standard), Chinese and Russian data, but has seen fewer tokens in these languages compared to the 6 above. We report the performance on these langages for information.
| Category | Metric | SmolLM3 3B Base | Qwen2.5-3B | Llama3.2 3B | Qwen3 1.7B Base | Qwen3 4B Base |
|---------|--------|---------------------|------------|--------------|------------------|---------------|
| Other supported languages | | | | | | | |
| Arabic| Belebele | 40.22 | 44.22 | <u>45.33</u> | 42.33 | **51.78** |
| | Global MMLU (CF) | 28.57 | 28.81 | 27.67 | <u>29.37</u> | **31.85** |
| | Flores-200 (5-shot) | <u>40.22</u> | 39.44 | **44.43** | 35.82 | 39.76 |
| Chinese| Belebele | 43.78 | 44.56 | <u>49.56</u> | 48.78 | **53.22** |
| | Global MMLU (CF) | 36.16 | 33.79 | <u>39.57</u> | 38.56 | **44.55** |
| | Flores-200 (5-shot) | 29.17 | **33.21** | 31.89 | 25.70 | <u>32.50</u> |
| Russian| Belebele | <u>47.44</u> | 45.89 | <u>47.44</u> | 45.22 | **51.44** |
| | Global MMLU (CF) | <u>36.51</u> | 32.47 | 34.52 | 34.83 | **38.80** |
| | Flores-200 (5-shot) | 47.13 | 48.74 | 50.74 | <u>54.70</u> | **60.53** |
## Training
### Model
- **Architecture:** Transformer decoder
- **Pretraining tokens:** 11T
- **Precision:** bfloat16
### Software & hardware
- **GPUs:** 384 H100
- **Training Framework:** [nanotron](https://github.com/huggingface/nanotron/tree/smollm3)
- **Data processing framework:** [datatrove](https://github.com/huggingface/datatrove)
- **Evaluation framework:** [lighteval](https://github.com/huggingface/lighteval)
- **Post-training Framework:** [TRL](https://github.com/huggingface/trl)
### Open resources
Here is an infographic with all the training details
- The datasets used for pretraining can be found in this [collection](https://huggingface.co/collections/HuggingFaceTB/smollm3-pretraining-datasets-685a7353fdc01aecde51b1d9) and those used in mid-training and post-training will be uploaded later
- The training and evaluation configs and code can be found in the [huggingface/smollm](https://github.com/huggingface/smollm) repository.
![image/png](https://cdn-uploads.huggingface.co/production/uploads/651e96991b97c9f33d26bde6/qiE5ZYr9SD1CIAtfEfuC8.png)
## Limitations
SmolLM3 can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content.
## License
[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)

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{#- Copyright 2025-present the Unsloth team. All rights reserved. #}
{#- Licensed under the Apache License, Version 2.0 (the "License") #}
{#- Edits made by Unsloth to make it work for most inference engines #}
{# ───── defaults ───── #}
{%- if enable_thinking is not defined -%}
{%- set enable_thinking = true -%}
{%- endif -%}
{# ───── reasoning mode ───── #}
{%- if enable_thinking -%}
{%- set reasoning_mode = "/think" -%}
{%- else -%}
{%- set reasoning_mode = "/no_think" -%}
{%- endif -%}
{# ───── header (system message) ───── #}
{{- "<|im_start|>system\n" -}}
{%- if messages[0].role == "system" -%}
{%- set system_message = messages[0].content -%}
{%- if "/no_think" in system_message -%}
{%- set reasoning_mode = "/no_think" -%}
{%- elif "/think" in system_message -%}
{%- set reasoning_mode = "/think" -%}
{%- endif -%}
{%- set custom_instructions = system_message.replace("/no_think", "") -%}
{%- set custom_instructions = custom_instructions.replace("/think", "") -%}
{%- set custom_instructions = custom_instructions.rstrip() -%}
{%- endif -%}
{%- if "/system_override" in system_message -%}
{%- set custom_instructions_x = custom_instructions.replace("/system_override", "") -%}
{{- custom_instructions_x.rstrip() -}}
{{- "<|im_end|>\n" -}}
{%- else -%}
{{- "## Metadata\n\n" -}}
{{- "Knowledge Cutoff Date: June 2025\n" -}}
{%- set today = strftime_now("%d %B %Y") -%}
{{- "Today Date: " + today + "\n" -}}
{{- "Reasoning Mode: " + reasoning_mode + "\n\n" -}}
{{- "## Custom Instructions\n\n" -}}
{%- if custom_instructions -%}
{{- custom_instructions + "\n\n" -}}
{%- elif reasoning_mode == "/think" -%}
{{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracking, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> Thought section </think> Solution section. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion.\n\n" -}}
{%- else -%}
{{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face.\n\n" -}}
{%- endif -%}
{%- if xml_tools is defined or python_tools is defined -%}
{{- "### Tools\n\n" -}}
{%- if xml_tools is defined -%}
{%- set ns = namespace(xml_tool_string="You may call one or more functions to assist with the user query.\nYou are provided with function signatures within <tools></tools> XML tags:\n\n<tools>\n") -%}
{%- for tool in xml_tools -%} {# The slicing makes sure that xml_tools is a list #}
{%- set ns.xml_tool_string = ns.xml_tool_string + (tool | string) + "\n" -%}
{%- endfor -%}
{%- set xml_tool_string = ns.xml_tool_string + "</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call>" -%}
{{- xml_tool_string -}}
{%- endif -%}
{%- if python_tools is defined -%}
{%- set ns = namespace(python_tool_string="When you send a message containing Python code between '<code>' and '</code>' tags, it will be executed in a stateful Jupyter notebook environment, and you will then be given the output to continued reasoning in an agentic loop.\n\nYou can use the following tools in your python code like regular functions:\n<tools>\n") -%}
{%- for tool in python_tools -%} {# The slicing makes sure that python_tools is a list #}
{%- set ns.python_tool_string = ns.python_tool_string + (tool | string) + "\n" -%}
{%- endfor -%}
{%- set python_tool_string = ns.python_tool_string + "</tools>\n\nThe state persists between code executions: so variables that you define in one step are still available thereafter." -%}
{{- python_tool_string -}}
{%- endif -%}
{{- "\n\n" -}}
{{- "<|im_end|>\n" -}}
{%- endif -%}
{%- endif -%}
{# ───── main loop ───── #}
{%- for message in messages -%}
{%- set content = message.content if message.content is string else "" -%}
{%- if message.role == "user" -%}
{{ "<|im_start|>" + message.role + "\n" + content + "<|im_end|>\n" }}
{%- elif message.role == "assistant" -%}
{%- if reasoning_mode == "/think" -%}
{{ "<|im_start|>assistant\n" + content.lstrip("\n") + "<|im_end|>\n" }}
{%- else -%}
{{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n" + content.lstrip("\n") + "<|im_end|>\n" }}
{%- endif -%}
{%- elif message.role == "tool" -%}
{{ "<|im_start|>" + "user\n" + content + "<|im_end|>\n" }}
{%- endif -%}
{%- endfor -%}
{# ───── generation prompt ───── #}
{%- if add_generation_prompt -%}
{%- if reasoning_mode == "/think" -%}
{{ "<|im_start|>assistant\n" }}
{%- else -%}
{{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n" }}
{%- endif -%}
{%- endif -%}
{#- Copyright 2025-present the Unsloth team. All rights reserved. #}
{#- Licensed under the Apache License, Version 2.0 (the "License") #}

109
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