boomer-1b

<div align="center"><img src="https://raw.githubusercontent.com/BudEcosystem/boomer/main/assets/boomer-logo.png" width=200></div> <p align="center"><i>Democratizing access to LLMs for the open-source community.<br>Let's advance AI, together. </i></p>

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Introduction 🎉

We are open-sourcing one of our early experiments of pretraining with custom architecture and datasets. This 1.1B parameter model is pre-trained from scratch using a custom-curated dataset of 41B tokens. The model's architecture experiments contain the addition of flash attention and a higher intermediate dimension of the MLP layer. The dataset is a combination of wiki, stories, arxiv, math and code. The model is available on huggingface Boomer1B

<div align="center"><img src="https://raw.githubusercontent.com/BudEcosystem/boomer/main/assets/boomer-arch.jpg" width=500></div>

Getting Started on GitHub 💻

Ready to dive in? Here's how you can get started with our models on GitHub.

Install the necessary dependencies with the following command:

pip install -r requirements.txt

Generate responses

Now that your model is fine-tuned, you're ready to generate responses. You can do this using our generate.py script, which runs inference from the Hugging Face model hub and inference on a specified input. Here's an example of usage:

python generate.py --base_model 'budecosystem/boomer-1b' --prompt="the president of India is"

Fine-tuning 🎯

It's time to upgrade the model by fine-tuning the model. You can do this using our provided finetune.py script. Here's an example command:

torchrun --nproc_per_node 4 train.py \
   --base_model budecosystem/boomer-1b \
   --data_path dataset.json \
   --output_dir output \
   --per_device_train_batch_size 2 \
   --gradient_accumulation_steps 2 \
   --num_train_epochs 1 \
   --learning_rate 2e-5 \
   --fp16 True \
   --logging_steps 10 \
   --deepspeed ds_config.json

Model details

Tokenizer

We used the SentencePiece tokenizer during the fine-tuning process. This tokenizer is known for its capability to handle open-vocabulary language tasks efficiently.

Training details

The model is trained of 4 A100 80GB for approximately 250hrs.

Evaluations

We have evaluated the pre-trained model on few of the benchmarks

Why use BOOMER?

• Retrieval augmentation
• Inference at the edge
• Language modeling use cases

Final thought on Boomer!

This isn't the end. It's just the beginning of a journey towards creating more advanced, more efficient, and more accessible language models. We invite you to join us on this exciting journey.

Aknowledgements

We'd like to thank the open-source community and the researchers whose foundational work laid the path for BOOMER. Special shoutout to our dedicated team who have worked relentlessly to curate the dataset and fine-tune the model to perfection.