Instructions to use HuggingFaceTB/SmolLM2-360M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceTB/SmolLM2-360M with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-360M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-360M")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-360M")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceTB/SmolLM2-360M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceTB/SmolLM2-360M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceTB/SmolLM2-360M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceTB/SmolLM2-360M

SGLang

How to use HuggingFaceTB/SmolLM2-360M 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/SmolLM2-360M" \
    --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/SmolLM2-360M",
		"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/SmolLM2-360M" \
        --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/SmolLM2-360M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceTB/SmolLM2-360M with Docker Model Runner:
```
docker model run hf.co/HuggingFaceTB/SmolLM2-360M
```

Reproducing Evaluation with lighteval

by PatrickHaller - opened Nov 18, 2024

Discussion

PatrickHaller

Nov 18, 2024

•

edited Nov 18, 2024

Hey!

For reproducibilities sake can you verify if this is the right configuration for evaluating with lighteval?

helm|hellaswag|0|0
lighteval|arc:easy|0|0
leaderboard|arc:challenge|0|0
helm|mmlu|0|0
helm|piqa|0|0
helm|commonsenseqa|0|0
lighteval|triviaqa|0|0
leaderboard|winogrande|0|0
lighteval|openbookqa|0|0
leaderboard|gsm8k|5|0

Furthermore:

Did you manually calculate average over the accuracy for easy and challenge for ARC?
What metrics did you report? Is it all accuracy?

Greetings,
Patrick

PatrickHaller

Nov 18, 2024

It also seems that some numbers might be wrong:

Winogrande 52.5 -> 54.62
GSM8K 3.2 -> 0.32
PIQA 71.3 -> 3.1 (em), 9.0 (qem), 3.8 (pem), 19.79 (pqem)
ETC.

Some numbers seems to be wildly different from what you reported....

|                     Task                      |Version| Metric |Value |   |Stderr|
|-----------------------------------------------|------:|--------|-----:|---|-----:|
|all                                            |       |em      |0.1994|±  |0.0285|
|                                               |       |qem     |0.2052|±  |0.0282|
|                                               |       |pem     |0.2423|±  |0.0308|
|                                               |       |pqem    |0.4098|±  |0.0352|
|                                               |       |acc     |0.4650|±  |0.0142|
|                                               |       |acc_norm|0.4796|±  |0.0151|
|helm:commonsenseqa:0                           |      0|em      |0.1949|±  |0.0113|
|                                               |       |qem     |0.1974|±  |0.0114|
|                                               |       |pem     |0.1949|±  |0.0113|
|                                               |       |pqem    |0.3129|±  |0.0133|
|helm:hellaswag:0                               |      0|em      |0.2173|±  |0.0041|
|                                               |       |qem     |0.2404|±  |0.0043|
|                                               |       |pem     |0.2297|±  |0.0042|
|                                               |       |pqem    |0.3162|±  |0.0046|
|helm:mmlu:_average:0                           |       |em      |0.2021|±  |0.0297|
|                                               |       |qem     |0.2109|±  |0.0303|
|                                               |       |pem     |0.2469|±  |0.0321|
|                                               |       |pqem    |0.4168|±  |0.0366|
--- MMLU subs ---
|helm:piqa:0                                    |      0|em      |0.0311|±  |0.0025|
|                                               |       |qem     |0.0904|±  |0.0041|
|                                               |       |pem     |0.0386|±  |0.0027|
|                                               |       |pqem    |0.1979|±  |0.0057|
|leaderboard:arc:challenge:0                    |      0|acc     |0.3660|±  |0.0141|
|                                               |       |acc_norm|0.3848|±  |0.0142|
|leaderboard:gsm8k:5                            |      0|qem     |0.0030|±  |0.0015|
|leaderboard:winogrande:0                       |      0|acc     |0.5462|±  |0.0140|
|lighteval:arc:easy:0                           |      0|acc     |0.7016|±  |0.0094|
|                                               |       |acc_norm|0.6801|±  |0.0096|
|lighteval:openbookqa:0                         |      0|acc     |0.2460|±  |0.0193|
|                                               |       |acc_norm|0.3740|±  |0.0217|
|lighteval:triviaqa:0                           |      0|qem     |0.1699|±  |0.0028|

loubnabnl

Hugging Face Smol Models Research org Nov 22, 2024

•

edited Nov 22, 2024

Hi, you can find the evaluation details: https://github.com/huggingface/smollm/blob/main/evaluation/README.md (currently missing MMLU cloze, we'll add it soon)

chungimungi

Mar 24, 2025

hey @loubnabnl the page link doesn't exist. Could you confirm @PatrickHaller numbers ?

loubnabnl

Hugging Face Smol Models Research org Apr 1, 2025

Here's an updated link https://github.com/huggingface/smollm/tree/main/text/evaluation#smollm2-base-models this gives the scores in the model card

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