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.
The 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using
UltraFeedback.
The instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by
Argilla such as
Synth-APIGen-v0.1.
1from transformers import AutoModelForCausalLM, AutoTokenizer
2checkpoint = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
3
4device = "cuda" # for GPU usage or "cpu" for CPU usage
5tokenizer = AutoTokenizer.from_pretrained(checkpoint)
6# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
7model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
8
9messages = [{"role": "user", "content": "What is the capital of France."}]
10input_text=tokenizer.apply_chat_template(messages, tokenize=False)
11inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
12outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
13print(tokenizer.decode(outputs[0]))
