Instructions to use HuggingFaceTB/SmolLM2-135M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceTB/SmolLM2-135M with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-135M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-135M")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HuggingFaceTB/SmolLM2-135M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceTB/SmolLM2-135M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceTB/SmolLM2-135M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceTB/SmolLM2-135M

SGLang

How to use HuggingFaceTB/SmolLM2-135M 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/SmolLM2-135M" \
    --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/SmolLM2-135M",
		"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/SmolLM2-135M" \
        --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/SmolLM2-135M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceTB/SmolLM2-135M with Docker Model Runner:
```
docker model run hf.co/HuggingFaceTB/SmolLM2-135M
```

Natural language lossless compressor using SmolLM2-135M

by robtacconelli - opened Feb 24

Discussion

robtacconelli

Feb 24

Hi everyone! 👋
Just wanted to share that SmolLM2-135M is the core model behind Nacrith, a lossless compression system that achieves the best compression results we've measured on natural language text — outperforming every classical and neural compressor tested, including CMIX, ts_zip, and FineZip (which uses an 8B model).
Some highlights with your 135M model at the center:

0.918 bpb on alice29.txt (−44% vs CMIX, −20% vs ts_zip)
0.9389 bpb on enwik8 100 MB (−8% vs FineZip's 8B fine-tuned model)
0.723 bpb on a document published after the training cutoff — confirming it's not memorization

The whole system runs on a GTX 1050 Ti with ~500 MB of GGUF weights. SmolLM2-135M hits a remarkable sweet spot: strong enough predictions to beat models 60× its size, small enough to fit on consumer hardware.

💻 Code: https://github.com/robtacconelli/Nacrith-GPU
⭐ Space: https://huggingface.co/spaces/robtacconelli/Nacrith-GPU
📄 Paper: https://arxiv.org/abs/2602.19626

Would love to hear your thoughts — and thank you for making SmolLM2 open! ❤️

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