base on Everything about the SmolLM and SmolVLM family of models # 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) - [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/HuggingFaceTB/smollm2-6723884218bcda64b34d7db9) - [SmolVLM Model](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct) ### Datasets - [SmolLM3 Pretraining dataset](https://huggingface.co/collections/HuggingFaceTB/smollm3-pretraining-datasets-685a7353fdc01aecde51b1d9) - [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) - Our instruction-tuning dataset - [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath) - Mathematics pretraining dataset - [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) - Educational content pretraining dataset ", Assign "at most 3 tags" to the expected json: {"id":"14250","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"