Instructions to use cortexso/smollm2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use cortexso/smollm2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf cortexso/smollm2:Q4_K_M # Run inference directly in the terminal: llama cli -hf cortexso/smollm2:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cortexso/smollm2:Q4_K_M # Run inference directly in the terminal: llama cli -hf cortexso/smollm2:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf cortexso/smollm2:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cortexso/smollm2:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf cortexso/smollm2:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cortexso/smollm2:Q4_K_M
Use Docker
docker model run hf.co/cortexso/smollm2:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use cortexso/smollm2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cortexso/smollm2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cortexso/smollm2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cortexso/smollm2:Q4_K_M
- Ollama
How to use cortexso/smollm2 with Ollama:
ollama run hf.co/cortexso/smollm2:Q4_K_M
- Unsloth Studio
How to use cortexso/smollm2 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cortexso/smollm2 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cortexso/smollm2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cortexso/smollm2 to start chatting
- Docker Model Runner
How to use cortexso/smollm2 with Docker Model Runner:
docker model run hf.co/cortexso/smollm2:Q4_K_M
- Lemonade
How to use cortexso/smollm2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cortexso/smollm2:Q4_K_M
Run and chat with the model
lemonade run user.smollm2-Q4_K_M
List all available models
lemonade list
- Atomic Chat
| license: apache-2.0 | |
| pipeline_tag: text-generation | |
| tags: | |
| - cortex.cpp | |
| ## Overview | |
| SmolLM2 is a family of compact language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are designed to solve a wide range of tasks while being lightweight enough for on-device deployment. More details can be found in the [SmolLM2 paper](https://arxiv.org/abs/2502.02737v1). | |
| The **1.7B variant** demonstrates significant improvements over its predecessor, SmolLM1-1.7B, especially in instruction following, knowledge retention, reasoning, and mathematical problem-solving. It was trained on **11 trillion tokens** using a diverse dataset combination, including **FineWeb-Edu, DCLM, The Stack**, and newly curated mathematics and coding datasets that will be released soon. | |
| The **instruct version** of SmolLM2 was developed through **supervised fine-tuning (SFT)** using a mix of public datasets and curated proprietary datasets. It further benefits from **Direct Preference Optimization (DPO)** using **UltraFeedback**. | |
| Additionally, the instruct model supports tasks such as **text rewriting, summarization, and function calling**, enabled by datasets from **Argilla**, including **Synth-APIGen-v0.1**. The SFT dataset is available at: [SmolTalk SFT Dataset](https://huggingface.co/datasets/HuggingFaceTB/smoltalk). | |
| For further details, visit the [SmolLM2 GitHub repository](https://github.com/huggingface/smollm), where you will find resources for **pre-training, post-training, evaluation, and local inference**. | |
| ## Variants | |
| | No | Variant | Cortex CLI command | | |
| | -- | ------------------------------------------------------ | ---------------------- | | |
| | 1 | [Smollm2-1.7b](https://huggingface.co/cortexso/smollm2/tree/1.7b) | `cortex run smollm2:1.7b` | | |
| ## Use it with Jan (UI) | |
| 1. Install **Jan** using [Quickstart](https://jan.ai/docs/quickstart) | |
| 2. Use in Jan model Hub: | |
| ```bash | |
| cortexhub/smollm2 | |
| ``` | |
| ## Use it with Cortex (CLI) | |
| 1. Install **Cortex** using [Quickstart](https://cortex.jan.ai/docs/quickstart) | |
| 2. Run the model with command: | |
| ```bash | |
| cortex run smollm2 | |
| ``` | |
| ## Credits | |
| - **Author:** SmolLM2 Team | |
| - **Converter:** [Homebrew](https://www.homebrew.ltd/) | |
| - **Original License:** [Apache 2.0](https://choosealicense.com/licenses/apache-2.0/) | |
| - **Papers:** [SmolLM2 Research](https://arxiv.org/abs/2502.02737v1) |