Instructions to use HuggingFaceTB/SmolLM3-3B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceTB/SmolLM3-3B-Base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="HuggingFaceTB/SmolLM3-3B-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM3-3B-Base")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM3-3B-Base")

Transformers.js

How to use HuggingFaceTB/SmolLM3-3B-Base with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('text-generation', 'HuggingFaceTB/SmolLM3-3B-Base');

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceTB/SmolLM3-3B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceTB/SmolLM3-3B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceTB/SmolLM3-3B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceTB/SmolLM3-3B-Base

SGLang

How to use HuggingFaceTB/SmolLM3-3B-Base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "HuggingFaceTB/SmolLM3-3B-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceTB/SmolLM3-3B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "HuggingFaceTB/SmolLM3-3B-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceTB/SmolLM3-3B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceTB/SmolLM3-3B-Base with Docker Model Runner:
```
docker model run hf.co/HuggingFaceTB/SmolLM3-3B-Base
```

eliebak HF Staff commited on Jul 8, 2025

Commit

731db12

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1 Parent(s): 44077b3

Update README.md

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@@ -96,8 +96,9 @@ Note: All evaluations are zero-shot unless stated otherwise.
 | | 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 context | 76.35 | 75.93 | <u>77.58</u> | 70.63 | **83.98** |
-| | Ruler 64k context | 67.85 | 64.90 | **72.93** | 57.18 | 60.29 |
 #### Multilingual benchmarks

 | | 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