Ablation Model Fineweb Edu

🚀 Model Card for HuggingFaceFW/ablation-model-fineweb-edu

This model card provides an overview of the HuggingFaceFW/ablation-model-fineweb-edu, including its summary, usage, training details, evaluation, and limitations.

✨ Features

• Part of the FineWeb ablations.
• Uses Llama architecture with RoPE.
• Trained on 350B tokens from FineWeb-Edu.
• Suitable for English text completion.

📦 Installation

To use this model, you need to install the transformers library. You can install it using the following command:

pip install -q transformers

💻 Usage Examples

Basic Usage

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

model = "HuggingFaceFW/ablation-model-fineweb-edu"
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]))

Advanced Usage

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

model = AutoModelForCausalLM.from_pretrained("HuggingFaceFW/ablation-model-fineweb-edu", revision="step-001000-2BT")

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

from huggingface_hub import list_repo_refs
out = list_repo_refs("HuggingFaceFW/ablation-model-fineweb-edu")
print([b.name for b in out.branches])

📚 Documentation

Model summary

This model is part of the FineWeb ablations, detailed in this technical report. The model has 1.82B parameters, 2048 context length and uses Llama architecture with RoPE. It was trained on 350B tokens from FineWeb-Edu, tokenized using gpt2 tokenizer.

• Paper: FineWeb: decanting the web for the finest text data at scale
• License: Apache-2
• Languages: English

Intended use

This model was trained on English web data and is not instruction-tuned, making it intended for text completion in English. It is important to note that the primary intended use case of this model is to compare its performance with other models trained under the same conditions. This model is not necessarily the best possible outcome achievable with the given dataset.

Intermediate checkpoints (soon)

We are releasing intermediate checkpoints for this model at intervals of every 1000 training steps in separate branches. The naming convention is step-001000-2BT.

Training

Model

Hardware

Software

• nanotron for training
• datatrove for tokenization
• lighteval for evaluation

Evaluation

We used the same setup to evaluate all our ablation models with lighteval. To reproduce our numbers, make sure to follow the instruction here.

# download https://huggingface.co/datasets/HuggingFaceFW/fineweb/blob/main/lighteval_tasks.py and run:
accelerate launch --num_processes=1 lighteval/run_evals_accelerate.py --model_args="pretrained=HuggingFaceFW/ablation-model-fineweb-edu" \
    --custom_tasks "lighteval_tasks.py" --output_dir [OUTPUTPATH] --max_samples 1000 \ 
    --tasks "custom|hellaswag|0|1,custom|winogrande|0|1,custom|piqa|0|1,custom|siqa|0|1,custom|openbookqa|0|1,custom|arc:easy|0|1,custom|arc:challenge|0|1,custom|commonsense_qa|0|1,custom|mmlu:abstract_algebra|0|1,custom|mmlu:anatomy|0|1,custom|mmlu:astronomy|0|1,custom|mmlu:business_ethics|0|1,custom|mmlu:clinical_knowledge|0|1,custom|mmlu:college_biology|0|1,custom|mmlu:college_chemistry|0|1,custom|mmlu:college_computer_science|0|1,custom|mmlu:college_mathematics|0|1,custom|mmlu:college_medicine|0|1,custom|mmlu:college_physics|0|1,custom|mmlu:computer_security|0|1,custom|mmlu:conceptual_physics|0|1,custom|mmlu:econometrics|0|1,custom|mmlu:electrical_engineering|0|1,custom|mmlu:elementary_mathematics|0|1,custom|mmlu:formal_logic|0|1,custom|mmlu:global_facts|0|1,custom|mmlu:high_school_biology|0|1,custom|mmlu:high_school_chemistry|0|1,custom|mmlu:high_school_computer_science|0|1,custom|mmlu:high_school_european_history|0|1,custom|mmlu:high_school_geography|0|1,custom|mmlu:high_school_government_and_politics|0|1,custom|mmlu:high_school_macroeconomics|0|1,custom|mmlu:high_school_mathematics|0|1,custom|mmlu:high_school_microeconomics|0|1,custom|mmlu:high_school_physics|0|1,custom|mmlu:high_school_psychology|0|1,custom|mmlu:high_school_statistics|0|1,custom|mmlu:high_school_us_history|0|1,custom|mmlu:high_school_world_history|0|1,custom|mmlu:human_aging|0|1,custom|mmlu:human_sexuality|0|1,custom|mmlu:international_law|0|1,custom|mmlu:jurisprudence|0|1,custom|mmlu:logical_fallacies|0|1,custom|mmlu:machine_learning|0|1,custom|mmlu:management|0|1,custom|mmlu:marketing|0|1,custom|mmlu:medical_genetics|0|1,custom|mmlu:miscellaneous|0|1,custom|mmlu:moral_disputes|0|1,custom|mmlu:moral_scenarios|0|1,custom|mmlu:nutrition|0|1,custom|mmlu:philosophy|0|1,custom|mmlu:prehistory|0|1,custom|mmlu:professional_accounting|0|1,custom|mmlu:professional_law|0|1,custom|mmlu:professional_medicine|0|1,custom|mmlu:professional_psychology|0|1,custom|mmlu:public_relations|0|1,custom|mmlu:security_studies|0|1,custom|mmlu:sociology|0|1,custom|mmlu:us_foreign_policy|0|1,custom|mmlu:virology|0|1,custom|mmlu:world_religions|0|1"

In particular the MMLU prompts are slightly different from those in lm-evaluation-harness and the Open LLM Leaderboard, more in this blogpost. We use prompt templates that provide better signal for small and non instruction tuned models.

Limitations

This model was predominantly trained on English 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.

📄 License

This model is released under the Apache-2 license.