---
license: apache-2.0
language:
- en
library_name: transformers
datasets:
- budecosystem/intellecta
---

<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>

----

## Introduction 🎉

We are open-sourcing one of our early experiments of <a href="https://arxiv.org/abs/2402.17764"> BitNet b1.58</a> paper. This 634m parameter model is pre-trained from scratch using a custom synthetic dataset of 5B tokens. The model's architecture experiments contain the modification of using higher depth and shallow configuration



## Run the model

Please note that, at the moment, `trust_remote_code=True` is required for running the model.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("budecosystem/boomer-bitnet-634m",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("budecosystem/boomer-bitnet-634m")

input_ids = tokenizer("In the recent Super Bowl LVIII,", return_tensors='pt').to(model.device)["input_ids"]

outputs = model.generate(input_ids, max_new_tokens=216)

print(tokenizer.batch_decode(outputs))
```

## Evaluations

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

| Model Name | ARC | MMLU | Winogrande | Hellaswag | MathQA   | GSM8K   |
|:----------:|:--------:|:----:|:----------:|:---------:|:-----: |:----:|
| boomer-bitnet-634m | 26.19 | 25.23 | 51.07 | 34.08 | 23.38 | 0.91 |


### 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 team who published BitNet b1.58 paper.