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

Libraries

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

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

pipe = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-135M-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Transformers.js

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

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

// Allocate pipeline
const pipe = await pipeline('text-generation', 'HuggingFaceTB/SmolLM2-135M-Instruct');

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceTB/SmolLM2-135M-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/SmolLM2-135M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use HuggingFaceTB/SmolLM2-135M-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/SmolLM2-135M-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/SmolLM2-135M-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/SmolLM2-135M-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/SmolLM2-135M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Why are model_q4.onnx and model_q4f16.onnx not 4 times smaller than model.onnx?

by Franck-Dernoncourt - opened Nov 7, 2024

Discussion

Franck-Dernoncourt

Nov 7, 2024

I see on https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct/tree/main/onnx:

File Name	Size
model.onnx	654 MB
model_fp16.onnx	327 MB
model_q4.onnx	200 MB
model_q4f16.onnx	134 MB

I understand that:

model.onnx is the fp32 model,
model_fp16.onnx is the model whose weights are quantized to fp16

I don't understand the size of model_q4.onnx and model_q4f16.onnx

Why is model_q4.onnx 200 MB instead of 654 MB / 4 = 163.5 MB? I thought model_q4.onnx meant that the weights are quantized to 4 bits.
Why is model_q4f16.onnx 134 MB instead of 654 MB / 4 = 163.5 MB? I thought model_q4f16.onnx meant that the weights are quantized to 4 bits and activations are fp16, since https://llm.mlc.ai/docs/compilation/configure_quantization.html states:

qAfB(_id), where A represents the number of bits for storing weights and B represents the number of bits for storing activations.

and Why do activations need more bits (16bit) than weights (8bit) in tensor flow's neural network quantization framework? indicates that activations don't count toward the model size (understandably).

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