ritvikbio commited on
Commit
946c4ab
·
verified ·
1 Parent(s): 33700be

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +26 -4
README.md CHANGED
@@ -1,10 +1,32 @@
1
  ---
2
- title: README
3
- emoji: 💻
4
  colorFrom: pink
5
  colorTo: purple
6
  sdk: static
7
- pinned: false
 
 
8
  ---
9
 
10
- Edit this `README.md` markdown file to author your organization card.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ title: Bio Protocol
3
+ emoji: 🧬
4
  colorFrom: pink
5
  colorTo: purple
6
  sdk: static
7
+ pinned: true
8
+ license: mit
9
+ short_description: Your Gateway Drug to Decentralized Science.
10
  ---
11
 
12
+ # Bio Protocol
13
+
14
+ Welcome to **Bio Protocol** 🧬, your gateway drug to decentralized science. We're pioneering the intersection of AI, biotechnology, and blockchain to democratize scientific research and innovation.
15
+
16
+ ## About Us
17
+
18
+ At Bio Protocol, we're a team of AI engineers and researchers dedicated to building open-source tools that accelerate decentralized science. Our work focuses on leveraging advanced AI techniques to make scientific data more accessible, verifiable, and collaborative. We blend cutting-edge machine learning with blockchain principles to create transparent, reproducible pipelines for research.
19
+
20
+ Our mission is to empower scientists, developers, and enthusiasts worldwide to contribute to a decentralized ecosystem where knowledge isn't siloed but shared freely under open licenses like MIT.
21
+
22
+ ## Our Projects
23
+
24
+ We specialize in AI-driven solutions for scientific workflows. Here are some of our key initiatives:
25
+
26
+ - **RAG Pipelines**: Implementing retrieval-augmented generation (RAG) systems inspired by OpenScholar to enhance knowledge retrieval from vast scientific literature. These pipelines enable efficient querying and synthesis of research data.
27
+
28
+ - **LLM Training**: Fine-tuning and training large language models (LLMs) tailored for biotech applications, such as analyzing genetic sequences, predicting protein structures, or automating literature reviews.
29
+
30
+ - **PDF Miner ML Models**: Developing machine learning models for extracting structured data from scientific PDFs. These tools help parse complex documents, extract tables, figures, and text, making them machine-readable for downstream AI tasks.
31
+
32
+ Explore our repositories on Hugging Face for models, datasets, and code you can use or contribute to.