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

Libraries

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

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

pipe = pipeline("text-generation", model="HuggingFaceTB/SmolLM-135M-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM-135M-Instruct")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM-135M-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceTB/SmolLM-135M-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceTB/SmolLM-135M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use HuggingFaceTB/SmolLM-135M-Instruct 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-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceTB/SmolLM-135M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceTB/SmolLM-135M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

SmolLM Performance

by lingzhiai - opened Aug 12, 2024

Discussion

lingzhiai

Aug 12, 2024

•

edited Aug 12, 2024

I’ve been working with SmolLM recently, and the performance has been far below expectations—it's practically unusable. Here are a few examples to illustrate the issues:

Could it be that I'm loading the model incorrectly, or is this a known issue with SmolLM? Any advice on what might be going wrong would be greatly appreciated.

loubnabnl

Hugging Face Smol Models Research org Aug 18, 2024

•

edited Aug 18, 2024

Hi, we just updated the Instruct Models and the outputs should be better. You can also try the larger 360M model for better performance in these demos:
https://huggingface.co/spaces/HuggingFaceTB/instant-smollm
https://huggingface.co/spaces/HuggingFaceTB/SmolLM-360M-Instruct-WebGPU

Daoguang

Aug 21, 2024

Thanks for the update! Could you please share what changes were made that led to the performance improvement? Was the model retrained with the original data, or were there other adjustments? Any details you can provide would be greatly appreciated. Thanks again for your help!

loubnabnl

Hugging Face Smol Models Research org Aug 21, 2024

•

edited Aug 22, 2024

We changed the SFT mix (see changelog):

it seems that using WebInstruct data for SFT sometimes confused the models, since it contained advanced science content beyond the model's capacity (hence why the models sometimes bring up math equations that are out of topic), so we switched to Magpie dataset
with Magpie the model would answer knwoledge prompts but still failed at answering greetings and "who are you" questions so we built this dataset of 2k simple everyday conversations to fix this behavior https://huggingface.co/datasets/HuggingFaceTB/everyday-conversations-llama3.1-2k

Daoguang

Aug 21, 2024

Thanks for your quick reply!

loubnabnl changed discussion status to closed Sep 4, 2024

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment