Update README.md

README.md

@@ -1,3 +1,58 @@
 ---
 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   | DROP  | GSM8K   |
+|:----------:|:--------:|:----:|:----------:|:---------:|:-----: |:-----:|:----:|
+| Boomer1B   | 22.35     | 25.92| 6.1      | 31.66     | 28.65   | 6.13   | 1.5 |
+| budecosystem/boomer-bitnet-634m | | | | | | | |
+
+
+### 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.