metadata

license: apache-2.0
datasets:
  - HuggingFaceTB/finemath
language:
  - en
base_model:
  - meta-llama/Llama-3.2-3B

Model Card

Model summary

This is a continual-pre-training of Llama-3.2-3B on a mix of 📐 FineMath (our new high quality math dataset) and FineWeb-Edu.

The model demonstrates superior math performance compared to Llama 3.2 3B, while maintaining similar performance on knowledge, reasoning, and common sense benchmarks:

It was trained on 160B tokens using a mix of 40% FineWeb-Edu and 60% from FineMath (30% FineMath-4+ subset and 30% InfiWebMath-4+ subset). We use nanotron for the training, and you can find the training scripts in our SmolLM2 GitHub repo.

Use

Intended use

This model was trained on English math data and is not instruction-tuned, making it intended for text completion in English.

Generation

# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model = "HuggingFaceTB/FineMath-Llama-3B"
device = "cuda" # for GPU usage or "cpu" for CPU usage

tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model).to(device)

inputs = tokenizer.encode("Machine Learning is", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

Intermediate checkpoints

We are releasing intermediate checkpoints for this model at intervals of every 10000 training steps (10B tokens) in separate branches. The naming convention is 10B.

You can load a specific model revision with transformers using the argument revision:

model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/FineMath-Llama-3B", revision="10B")

You can access all the revisions for the models via the following code:

from huggingface_hub import list_repo_refs
out = list_repo_refs("HuggingFaceTB/FineMath-Llama-3B")
print([b.name for b in out.branches])

Training

Model

Architecture: Llama3
Pretraining steps: 160k
Pretraining tokens: 160B
Precision: bfloat16

Hardware

GPUs: 64 H100

Software

nanotron for training
datatrove for tokenization
lighteval for evaluation

Evaluation

We used the SmolLM2 setup to evaluate all our ablation models with lighteval. You can find the details here: https://github.com/huggingface/smollm/tree/main/evaluation#smollm2-base-models

Limitations

This model was predominantly trained on English math data, potentially limiting its performance in other languages. Furthermore, the model's behavior is influenced by the quality and diversity of its training data, which may include biases and harmful content.