Update README.md

README.md

@@ -25,73 +25,9 @@ At Vectorphile, our core mission revolves around three pillars:
 
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-## 🚀 What We Do
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-We specialize in the end-to-end development of AI models and resources, with a primary focus on:
-
-### 🧠 Open-Source Language Models (LLMs)
-We design, train, and fine-tune a diverse range of open-source LLMs, from general-purpose models suitable for a wide array of tasks to specialized variants optimized for specific domains or applications. Our models are engineered for:
-* **High Performance:** Achieving competitive benchmarks across various NLP tasks.
-* **Efficiency:** Optimizing models for deployment on diverse hardware, from cloud GPUs to edge devices.
-* **Accessibility:** Ensuring models are easy to integrate and use within existing AI workflows.
-
-### ✨ Advanced Embedding Models
-Embeddings are the bedrock of modern AI, capturing semantic meaning in dense vector spaces. Vectorphile develops high-quality embedding models that are:
-* **Context-Aware:** Excelling in capturing nuanced relationships between words, phrases, and documents.
-* **Multilingual:** Supporting a wide array of languages to promote global AI accessibility.
-* **Versatile:** Applicable to tasks such as semantic search, clustering, recommendation systems, and more.
-
-### 🛠️ LLM Ecosystem Contributions
-Beyond models, we contribute to the broader LLM ecosystem by developing and sharing:
-* **Datasets:** Curated and high-quality datasets for training and evaluating LLMs and embeddings.
-* **Tools & Utilities:** Open-source libraries and frameworks to streamline model development, fine-tuning, and deployment.
-* **Research Papers:** Publishing our findings and methodologies to advance collective knowledge in AI.
-
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-
-## 💡 Our Philosophy
-
-Our work is guided by a strong belief in:
-
-* **Community Collaboration:** We actively engage with the open-source community, welcoming contributions, feedback, and partnerships.
-* **Research Excellence:** We are committed to rigorous research and continuous improvement, ensuring our models are built on sound scientific principles.
-* **Responsible AI:** We prioritize ethical considerations, bias mitigation, and transparency in all our AI development efforts.
-
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-
-## 📂 Our Models on Hugging Face
-
-Explore our growing collection of open-source models, datasets, and spaces directly on our Hugging Face profile. Each model comes with detailed documentation, usage examples, and performance benchmarks.
-
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-
-Feel free to browse our repositories and integrate our models into your projects!
-
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-
-## 👋 Get Involved
-
-We believe that the best advancements happen through collaboration. Here's how you can join us:
-
-* **Contribute:** Submit pull requests to our repositories, whether it's code, documentation, or new model ideas.
-* **Feedback:** Share your insights, report issues, or suggest improvements for our models and tools.
-* **Connect:** Join our community discussions to stay updated on our latest developments and engage with fellow AI enthusiasts.
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 ## 📧 Connect With Us
 
 * **Website:** [https://www.vectorphile.com](https://www.vectorphile.com) 
 * **GitHub:** [https://github.com/vectorphile](https://github.com/vectorphile) 
 * **LinkedIn:** [https://www.linkedin.com/company/vectorphile](https://www.linkedin.com/company/vectorphile) 
 * **Email:** contact@vectorphile.com 
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-## 📜 License
-
-All models and code released by Vectorphile on Hugging Face are licensed under the Apache 2.0 License, unless otherwise specified in individual model cards.
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-Thank you for being a part of the Vectorphile journey!