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HuggingFaceTB

HuggingFaceTB/SmolLM-135M

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Model Documentation

SmolLM



SmolLM


Table of Contents



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

Model Summary



SmolLM is a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are built on Cosmo-Corpus, a meticulously curated high-quality training dataset. Cosmo-Corpus includes Cosmopedia v2 (28B tokens of synthetic textbooks and stories generated by Mixtral), Python-Edu (4B tokens of educational Python samples from The Stack), and FineWeb-Edu (220B tokens of deduplicated educational web samples from FineWeb). SmolLM models have shown promising results when compared to other models in their size categories across various benchmarks testing common sense reasoning and world knowledge. For detailed information on training, benchmarks and performance, please refer to our full blog post.

This is the SmolLM-135M

Generation

bash
pip install transformers


#

Running the model on CPU/GPU/multi GPU

* _Using full precision_ 
python

pip install transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM-135M"
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)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

bash
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
Memory footprint: 12624.81 MB
* _Using torch.bfloat16_ 
python

pip install accelerate

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
checkpoint = "HuggingFaceTB/SmolLM-135M"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

for fp16 use torch_dtype=torch.float16 instead

model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

bash
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
Memory footprint: 269.03 MB


#

Quantized Versions through bitsandbytes

* _Using 8-bit precision (int8)_

python

pip install bitsandbytes accelerate

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

to use 4bit use load_in_4bit=True instead

quantization_config = BitsAndBytesConfig(load_in_8bit=True)
checkpoint = "HuggingFaceTB/SmolLM-135M"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, quantization_config=quantization_config)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

bash
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")

load_in_8bit

Memory footprint: 162.87 MB

load_in_4bit

>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
Memory footprint: 109.78 MB


Limitations



While SmolLM models have been trained on a diverse dataset including educational content and synthetic texts, they have limitations. The 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. For a more comprehensive discussion of the models' capabilities and limitations, please refer to our full blog post..

This repository contains a converted version of our latest trained model. We've noticed a small performance difference between this converted checkpoint (transformers) and the original (nanotron). We're currently working to resolve this issue.

Training



Model



  • Architecture: For architecture detail, see the blog post.
  • Pretraining steps: 600k
  • Pretraining tokens: 600B
  • Precision: bfloat16
  • Tokenizer: HuggingFaceTB/cosmo2-tokenizer


    • Hardware



  • GPUs: 64 H100


    • Software



  • Training Framework: Nanotron


    • License



    Apache 2.0

    Citation

    bash
@misc{allal2024SmolLM,
      title={SmolLM 
  • blazingly fast and remarkably powerful}, 
    • 
      author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Leandro von Werra and Thomas Wolf},
      year={2024},
}

    Files & Weights

    FilenameSizeAction
    model.safetensors 0.50 GB