SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device.

New: Introducing Smol-tools 🚀

1. Usage
   • Transformers
   • Chat in TRL
   • Local applications
2. Smol-tools
3. Fine-tuning
4. Evaluation

Our most powerful model is SmolLM2-1.7B-Instruct, which you can use as an assistant with transformers, trl, or using quantized versions with tools like llama.cpp, MLX, and transformers.js. For lighter applications, you can also use the smaller models SmolLM2-360M andSmolLM2-135M, which are suitable for on-device usage and can be integrated similarly. All available in this collection.

pip install transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM2-1.7B-Instruct"

device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

messages = [{"role": "user", "content": "Write a 100-word article on 'Benefits of Open-Source in AI research"}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0]))

You can also use the TRL CLI to chat with the model from the terminal:

pip install trl
trl chat --model_name_or_path HuggingFaceTB/SmolLM2-1.7B-Instruct --device cpu

You can find more details on how to leverage the model for use cases such as text summarization, text rewriting and function calling in the model card: https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct

You can use the models locally with frameworks like llama.cpp, MLX, and transformers.js, which support SmolLM2. All models are available in this collection.

A collection of lightweight AI-powered tools built with LLaMA.cpp and small language models. These tools are designed to run locally on your machine without requiring expensive GPU resources. Further instructions on how to use the tools can be found in the smol-tools README.

You can find an example script to finetune SmolLM2 using TRL and PEFT in the finetune folder.

You can find more detailed evaluation of each model size in the model cards in this collection. We use lighteval for all our evaluations, more details coming soon.