---
license: apache-2.0
language:
- en
library_name: transformers
---
<div align="center"><img src="https://github.com/BudEcosystem/boomer/blob/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>

----

## 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](https://huggingface.co/budecosystem/boomer-1b)

<div align="center"><img src="https://github.com/BudEcosystem/boomer/blob/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:

```bash
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:

```bash
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:

```bash
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

| Parameters  | Value  |
| :-------------  | :----: |
| n_layers        | 4     |
| n_heads         | 32     |
| d_model         | 4096   |
| vocab size      | 32000 |
| sequence length | 4096   |
| Intermediate size | 11008 |

### 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. 

| Hyperparameters              | Value  |
| :----------------------------| :-----: |
| per_device_train_batch_size  | 2      |
| gradient_accumulation_steps  | 2      |
| learning_rate                | 2e-4   |
| optimizer                    | adamw  |
| beta                         | 0.9, 0.95 |
| fp16                         | True   |
| GPU                          | 4 A100 80GB |


## Evaluations

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

| Model Name | ARC | MMLU | Human Eval | Hellaswag | BBH   | DROP  | GSM8K   |
|:----------:|:--------:|:----:|:----------:|:---------:|:-----: |:-----:|:----:|
| Boomer1B   | 22.35     | 25.92| 6.1      | 31.66     | 28.65   | 6.13   | 1.5 |

### 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.