jncraton/SmolLM2-360M-Instruct-ct2-int8

SmolLM2

Table of Contents

1. Model Summary
2. Limitations
3. Training
4. License
5. Citation

Model Summary

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.

SmolLM2 demonstrates significant advances over its predecessor SmolLM1, particularly in instruction following, knowledge, reasoning. The 360M model was trained on 4 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets 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.

How to use

Transformers

pip install transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM2-360M-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": "What is the capital of France."}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)
print(input_text)
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]))

Chat in TRL

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/SmolLM2-360M-Instruct --device cpu

Evaluation

In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use lighteval to run them.

Base Pre-Trained Model

Metrics	SmolLM2-360M	Qwen2.5-0.5B	SmolLM-360M
HellaSwag	54.5	51.2	51.8
ARC (Average)	53.0	45.4	50.1
PIQA	71.7	69.9	71.6
MMLU (cloze)	35.8	33.7	34.4
CommonsenseQA	38.0	31.6	35.3
TriviaQA	16.9	4.3	9.1
Winogrande	52.5	54.1	52.8
OpenBookQA	37.4	37.4	37.2
GSM8K (5-shot)	3.2	33.4	1.6

Instruction Model

Metric	SmolLM2-360M-Instruct	Qwen2.5-0.5B-Instruct	SmolLM-360M-Instruct
IFEval (Average prompt/inst)	41.0	31.6	19.8
MT-Bench	3.66	4.16	3.37
HellaSwag	52.1	48.0	47.9
ARC (Average)	43.7	37.3	38.8
PIQA	70.8	67.2	69.4
MMLU (cloze)	32.8	31.7	30.6
BBH (3-shot)	27.3	30.7	24.4
GSM8K (5-shot)	7.43	26.8	1.36

Limitations

SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content.

Training

Model

• Architecture: Transformer decoder
• Pretraining tokens: 4T
• Precision: bfloat16

Hardware

• GPUs: 64 H100

Software

• Training Framework: nanotron

License

Apache 2.0

Citation

@misc{allal2024SmolLM2,
      title={SmolLM2 - with great data, comes great performance}, 
      author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Lewis Tunstall and Agustín Piqueres and Andres Marafioti and Cyril Zakka and Leandro von Werra and Thomas Wolf},
      year={2024},
}