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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceTB/SmolLM-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/SmolLM-135M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

Data Formatting

#21

by moutasem - opened May 20, 2025

Discussion

moutasem

May 20, 2025

•

edited May 20, 2025

Hi,

I noticed that the Tokenizer does not have a chat template. How should I format my data?

Should it be something like this:

{'prompt': 'Remove all grammatical errors from this text: For example, countries with a lot of deserts can terraform their desert to increase their habitable land and using irrigation to provide clean water to the desert.',
 'completion': 'For example, countries with a lot of deserts can transform their desert to increase their habitable land and use irrigation to provide clean water to the desert.'}

Or should I add special tokens like so?

{'prompt': '<|im_start|>user\nRemove all grammatical errors from this text: For example, countries with a lot of deserts can terraform their desert to increase their habitable land and using irrigation to provide clean water to the desert.\n<|im_end|>\n<|im_start|>assistant\n',
 'completion': 'For example, countries with a lot of deserts can transform their desert to increase their habitable land and use irrigation to provide clean water to the desert.\n<|im_end|>'}

Also, should I leave both prompt and completion in the dataset and pass them to the SFTTrainer?

Thanks!!

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