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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Everything about the SmolLM and SmolVLM family of models
| Date | Stars |
|---|---|
| 2026-07-31 | 3859 |
| 2026-08-03 | 3860 |
| 2026-08-04 | 3862 |
| 2026-08-05 | 3864 |
| 2026-08-06 | 3865 |
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# Smol Models 🤏
Welcome to Smol Models, a family of efficient and lightweight AI models from Hugging Face. Our mission is to create fully open powerful yet compact models, for text and vision, that can run effectively on-device while maintaining strong performance.
## [NEW] SmolLM3 (Language Model)
Our 3B model outperforms Llama 3.2 3B and Qwen2.5 3B while staying competitive with larger 4B alternatives (Qwen3 & Gemma3). Beyond the performance numbers, we're sharing exactly how we built it using public datasets and training frameworks.
Ressources:
- [SmolLM3-Base](https://hf.co/HuggingFaceTB/SmolLM3-3B-Base)
- [SmolLM3](https://hf.co/HuggingFaceTB/SmolLM3-3B)
- [blog](https://hf.co/blog/smollm3)
Summary:
- **3B model** trained on 11T tokens, SoTA at the 3B scale and competitive with 4B models
- **Fully open model**, open weights + full training details including public data mixture and training configs
- **Instruct model** with **dual mode reasoning,** supporting think/no_think modes
- **Multilingual support** for 6 languages: English, French, Spanish, German, Italian, and Portuguese
- **Long context** up to 128k with NoPE and using YaRN
## 👁️ SmolVLM (Vision Language Model)
[SmolVLM](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct) is our compact multimodal model that can:
- Process both images and text and perform tasks like visual QA, image description, and visual storytelling
- Handle multiple images in a single conversation
- Run efficiently on-device
## Repository Structure
```
smollm/
├── text/ # SmolLM3/2/1 related code and resources
├── vision/ # SmolVLM related code and resources
└── tools/ # Shared utilities and inference tools
├── smol_tools/ # Lightweight AI-powered tools
├── smollm_local_inference/
└── smolvlm_local_inference/
```
## Getting Started
### SmolLM3
```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))
```
### SmolVLM
```python
from transformers import AutoProcessor, AutoModelForVision2Seq
processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM-Instruct")
model = AutoModelForVision2Seq.from_pretrained("HuggingFaceTB/SmolVLM-Instruct")
messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "What's in this image?"}
]
}
]
```
## Ecosystem
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/RvHjdlRT5gGQt5mJuhXH9.png" width="700"/>
</div>
## Resources
### Documentation
- [SmolLM3 Documentation](text/README.md)
- [SmolLM2 paper](https://arxiv.org/abs/2502.02737v1)
- [SmolVLM Documentation](vision/README.md)
- [Local Inference Guide](tools/README.md)
### Pretrained Models
- [SmolLM3 Models Collection](https://huggingface.co/collections/HuggingFaceTB/smollm3-686d33c1fdffe8e635317e23)
- [SmolLM2 Models Collection](https://huggingface.co/collections/HuggingFExcerpt of 4,585 characters
Read on GitHub
Loubna Ben Allal · @huggingface · France
70
elie · @PrimeIntellect-ai · France
40
Andrés Marafioti · Hugging Face · Switzerland
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Anton Lozhkov · Hugging Face · France
16
@huggingface · Switzerland
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Quentin Lhoest · Hugging Face · France
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Merve Noyan · @huggingface · France
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Ikko Eltociear Ashimine · Japan
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Pauline Bailly-Masson
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Kumar Shivendu · @Qdrant · India
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Nouamane Tazi · @huggingface · France
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Gabriel Martín Blázquez · @supersonik-ai · Spain
2
2
Alvaro Bartolome · @huggingface · Spain
1
Emmanuel Ferdman · Israel
1
1
1
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8dd440480e601283, llm:Repository description: 'Everything about the SmolLM and SmolVLM family of models' — Hugging Face models family (LM and VLM). No topics provided.
matched fp:8dd440480e601283, llm:Repository description: 'Everything about the SmolLM and SmolVLM family of models' — Hugging Face models family (LM and VLM). No topics provided.
matched fp:8dd440480e601283, llm:Repository description: 'Everything about the SmolLM and SmolVLM family of models' — Hugging Face models family (LM and VLM). No topics provided.