Instructions to use HuggingFaceTB/SmolLM2-1.7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceTB/SmolLM2-1.7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-1.7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-1.7B-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]:]))

Transformers.js

How to use HuggingFaceTB/SmolLM2-1.7B-Instruct with Transformers.js:

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

// Allocate pipeline
const pipe = await pipeline('text-generation', 'HuggingFaceTB/SmolLM2-1.7B-Instruct');

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceTB/SmolLM2-1.7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceTB/SmolLM2-1.7B-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/SmolLM2-1.7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HuggingFaceTB/SmolLM2-1.7B-Instruct

SGLang

How to use HuggingFaceTB/SmolLM2-1.7B-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/SmolLM2-1.7B-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/SmolLM2-1.7B-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/SmolLM2-1.7B-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/SmolLM2-1.7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HuggingFaceTB/SmolLM2-1.7B-Instruct with Docker Model Runner:
```
docker model run hf.co/HuggingFaceTB/SmolLM2-1.7B-Instruct
```

Performance on MATH dataset?

by fzyzcjy - opened Oct 31, 2024

Discussion

fzyzcjy

Oct 31, 2024

Hi thanks for the LLM! I would appreciate it if I could know the MATH performance on SmolLM2 series (currently seems only GSM8K).

loubnabnl

Hugging Face Smol Models Research org Nov 4, 2024

Hi HuggingFaceTB/SmolLM2-1.7B-Instruct scores 16.72 on MATH (4-shot)

fzyzcjy

Nov 4, 2024

@loubnabnl Hi, thank you very much! Btw, it seems that Llama-3.2-1B is 30.6 on MATH, and Qwen2.5-1.5B is 55.2 on MATH. Therefore, I wonder whether huggingface will create some models that is stronger in math in the future?

ypzkteknoloji

Nov 4, 2024

This comment has been hidden

loubnabnl

Hugging Face Smol Models Research org Nov 4, 2024

•

edited Nov 4, 2024

Evaluation setups can be different, in ours (which we'll share soon) Llama3.2-1B-Instruct scores 6.48 on MATH and Qwen2.5-1.5B-Instruct scores 31.07, so the model is already good at math for 1B models and we will continue to improve it in the next iterations

fzyzcjy

Nov 4, 2024

Thank you! That's interesting - I personally reproduced zero-shot cot llama 3.2-1B to be 27.8 etc. Looking forward to your evaluation setups!

ldwang

Nov 7, 2024

Looking forward to your evaluation setups! +1

anton-l

Hugging Face Smol Models Research org Nov 19, 2024

•

edited Nov 26, 2024

UPD: the code is merged into smollm/evaluation

~~The MATH task will likely be updated in the mainline lighteval, but in the meantime you could add the task code to smollm/evaluation/tasks.py~~

~~And run it with~~

lighteval accelerate \
  --model_args "pretrained=HuggingFaceTB/SmolLM2-1.7B-Instruct,revision=main,dtype=bfloat16,vllm,gpu_memory_utilisation=0.8,max_model_length=2048" \
  --custom_tasks "tasks.py" --tasks "custom|math|4|1" --use_chat_template --output_dir "./evals" --save_details

loubnabnl changed discussion status to closed Nov 29, 2024

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