[whatmodel.sh](../../README.md) / SmolLM2 360M Instruct
# SmolLM2 360M Instruct
Hugging Face · released 2024-10 · 0.362B params · apache-2.0
**score 21.7** ±2.7 · #697 of 732 · from 6 benchmark results across 2 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks SmolLM2 360M Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #349 of 402* 9.3% 1
math ●··········█ #2 of 2* 36% 1
chat & writing · #349 of 402 · 9.3% of its score
Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [QwQ-32B-Preview](qwq-32b-preview.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
IFEval (aggregation unspecified) … 41.0% ▁●▄▂▂▄▆██▅▂▁ beats 2/145 easy · avg 76% 9.3% at its level · small run
math · #2 of 2 · 36% of its score
test result vs the others in its run difficulty counts why
GSM8K (maker) 7.4% ▂▂▂●▃▄▅▇██▆▄ beats 29/206 easy · avg 82% 36% sharp · small run
other tests (4)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 27.3% ▁▁●▁▂▄▅▆█▃▃▁ beats 4/155 medium · avg 62% 32% sharp · too hard for it · small run
HellaSwag (maker) 52.1% ▁▁▁▂▂●▄▆█▇▄▂ beats 25/135 easy · avg 83% 12% at its level · small run
PIQA (maker) 70.8% ▁▁▁▂●▃▅█▅▃▁▁ beats 23/123 easy · avg 85% 5.1% blunt · at its level · small run
EuroEval HellaSwag (English), tes… 24.9% ●·▁▁▂▁▂▅█▇▄▁ beats 6/100 medium · avg 67% 2.6% sharp · too hard for it · noisy
source results share of the score
HuggingFaceTB/SmolLM2-360M-Instruct mod… 5 94% ▇▇▇▇▇▇▇▇▇▇▇
EuroEval leaderboards 1 3% ▇
prior: pull toward the middle 3% ▇
weighted average = 21.7
Licences: HuggingFaceTB/SmolLM2-360M-Instruct model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run SmolLM2 360M Instruct (Q8_0, needs ~0.39 GB, from [huggingface.co/HuggingFaceTB](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct-GGUF/tree/593b5a2e04c8f3e4ee880263f93e0bd2901ad47f))
```sh
llama cli -hf HuggingFaceTB/SmolLM2-360M-Instruct-GGUF:Q8_0
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf HuggingFaceTB/SmolLM2-360M-Instruct-GGUF:Q8_0
```
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run SmolLM2 360M Instruct (4bit, needs ~0.2 GB, Mac only, from [huggingface.co/Irfanuruchi](https://huggingface.co/Irfanuruchi/SmolLM2-360M-Instruct-MLX-4bit/tree/5568cb66857d23db47189d26b4ed645168bbdeaf))
```sh
lms get https://huggingface.co/Irfanuruchi/SmolLM2-360M-Instruct-MLX-4bit -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~0.39 GB) `lms get https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct-GGUF@Q8_0 -y` · 8bit (~0.4 GB, Mac only) `lms get https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct-Q8-mlx -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run SmolLM2 360M Instruct (fp16, needs ~0.726 GB, from [ollama.com](https://ollama.com/library/smollm2/tags))
```sh
ollama run smollm2:360m
```
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run SmolLM2 360M Instruct (4bit, needs ~0.2 GB, Mac only, from [huggingface.co/Irfanuruchi](https://huggingface.co/Irfanuruchi/SmolLM2-360M-Instruct-MLX-4bit/tree/5568cb66857d23db47189d26b4ed645168bbdeaf))
```sh
mlx_lm.chat --model Irfanuruchi/SmolLM2-360M-Instruct-MLX-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model Irfanuruchi/SmolLM2-360M-Instruct-MLX-4bit
```
Other versions: 8bit (~0.4 GB, Mac only) `mlx_lm.chat --model HuggingFaceTB/SmolLM2-360M-Instruct-Q8-mlx`
Model repository and documentation: [huggingface.co/HuggingFaceTB](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct)
[interactive version](https://whatmodel.sh/model/smollm2-360m) · [JSON](https://whatmodel.sh/api?format=json&all=1)