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README.md

@@ -1,199 +1,422 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+language:
+- en
+- fr
+- es
+- it
+- pt
+- zh
+- ar
+- ru
+base_model:
+- HuggingFaceTB/SmolLM3-3B-Base
+tags:
+- heretic
+- uncensored
+- decensored
+- abliterated
 ---
+# This is a decensored version of [HuggingFaceTB/SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B), made using [Heretic](https://github.com/p-e-w/heretic) v1.1.0
 
-# Model Card for Model ID
+## Abliteration parameters
 
-<!-- Provide a quick summary of what the model is/does. -->
+| Parameter | Value |
+| :-------- | :---: |
+| **direction_index** | 21.74 |
+| **attn.o_proj.max_weight** | 1.49 |
+| **attn.o_proj.max_weight_position** | 22.07 |
+| **attn.o_proj.min_weight** | 1.19 |
+| **attn.o_proj.min_weight_distance** | 7.61 |
+| **mlp.down_proj.max_weight** | 1.24 |
+| **mlp.down_proj.max_weight_position** | 29.57 |
+| **mlp.down_proj.min_weight** | 1.07 |
+| **mlp.down_proj.min_weight_distance** | 1.16 |
 
+## Performance
 
+| Metric | This model | Original model ([HuggingFaceTB/SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B)) |
+| :----- | :--------: | :---------------------------: |
+| **KL divergence** | 0.0001 | 0 *(by definition)* |
+| **Refusals** | 60/100 | 82/100 |
 
-## Model Details
+-----
 
-### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+# SmolLM3
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/zy0dqTCCt5IHmuzwoqtJ9.png)
 
-<!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+##  Table of Contents
 
-## Uses
+1. [Model Summary](#model-summary)
+2. [How to use](#how-to-use)
+3. [Evaluation](#evaluation)
+4. [Training](#training)
+5. [Limitations](#limitations)
+6. [License](#license)
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## Model Summary
 
-### Direct Use
+SmolLM3 is a 3B parameter language model designed to push the boundaries of small models. It supports dual mode reasoning, 6 languages and long context. SmolLM3 is a fully open model that offers strong performance at the 3B–4B scale.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/db3az7eGzs-Sb-8yUj-ff.png)
 
-[More Information Needed]
+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).
 
-### Downstream Use [optional]
+### 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 supports up to **128k tokens** using YARN extrapolation
+- **Multilingual**: 6 natively supported (English, French, Spanish, German, Italian, and Portuguese)
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+For more details refer to our blog post: https://hf.co/blog/smollm3
 
-[More Information Needed]
+## How to use
 
-### Out-of-Scope 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
+pip install -U transformers
+```
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-[More Information Needed]
+model_name = "HuggingFaceTB/SmolLM3-3B"
+device = "cuda"  # for GPU usage or "cpu" for CPU usage
 
-## Bias, Risks, and Limitations
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+).to(device)
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+# prepare the model input
+prompt = "Give me a brief explanation of gravity in simple terms."
+messages_think = [
+    {"role": "user", "content": prompt}
+]
 
-[More Information Needed]
+text = tokenizer.apply_chat_template(
+    messages_think,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
 
-### Recommendations
+# Generate the output
+generated_ids = model.generate(**model_inputs, max_new_tokens=32768)
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+# 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))
+```
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+>[!TIP]
+> We recommend setting `temperature=0.6` and `top_p=0.95` in the sampling parameters.
 
-## How to Get Started with the Model
+### Long context processing
 
-Use the code below to get started with the model.
+The current `config.json` is set for context length up to 65,536 tokens. To handle longer inputs (128k or 256k), we utilize YaRN you can change the `max_position_embeddings` and rope_scaling` to:
+```
+{
+  ...,
+  "rope_scaling": {
+    "factor": 2.0, #2x65536=131 072 
+    "original_max_position_embeddings": 65536,
+    "type": "yarn"
+  }
+}
+```
 
-[More Information Needed]
 
-## Training Details
+### Enabling and Disabling Extended Thinking Mode
 
-### Training Data
+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`.
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+```python
+prompt = "Give me a brief explanation of gravity in simple terms."
+messages = [
+    {"role": "system", "content": "/no_think"},
+    {"role": "user", "content": prompt}
+]
 
-[More Information Needed]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+```
 
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+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`, `MLC` and `ExecuTorch`. 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 --enable-auto-tool-choice --tool-call-parser=hermes
+```
+
+#### Setting `chat_template_kwargs`
+
+You can specify `chat_template_kwargs` such as `enable_thinking` 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
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+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> | 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.
+- The training intermediate checkpoints (including the mid-training and SFT checkpoints) are available at [HuggingFaceTB/SmolLM3-3B-checkpoints](https://huggingface.co/HuggingFaceTB/SmolLM3-3B-checkpoints)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/651e96991b97c9f33d26bde6/qiE5ZYr9SD1CIAtfEfuC8.png)
+
+### EU Summary of Public Content
+
+The EU AI Act requires all GPAI models to provide a Public Summary of Training Content according to a [given template](https://digital-strategy.ec.europa.eu/en/library/explanatory-notice-and-template-public-summary-training-content-general-purpose-ai-models).
+You can find the summary for this model below, as well as in its [development Space](https://huggingface.co/spaces/hfmlsoc/smollm3-eu-data-transparency).
+
+<iframe
+	src="https://hfmlsoc-smollm3-eu-data-transparency.hf.space"
+	frameborder="0"
+	width="850"
+	height="350"
+></iframe>
+
+
+## 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)
+
+## Citation
+```bash
+@misc{bakouch2025smollm3,
+  title={{SmolLM3: smol, multilingual, long-context reasoner}},
+  author={Bakouch, Elie and Ben Allal, Loubna and Lozhkov, Anton and Tazi, Nouamane and Tunstall, Lewis and Patiño, Carlos Miguel and Beeching, Edward and Roucher, Aymeric and Reedi, Aksel Joonas and Gallouédec, Quentin and Rasul, Kashif and Habib, Nathan and Fourrier, Clémentine and Kydlicek, Hynek and Penedo, Guilherme and Larcher, Hugo and Morlon, Mathieu and Srivastav, Vaibhav and Lochner, Joshua and Nguyen, Xuan-Son and Raffel, Colin and von Werra, Leandro and Wolf, Thomas},
+  year={2025},
+  howpublished={\url{https://huggingface.co/blog/smollm3}}
+}
+```