Update README.md

README.md

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+# **Triangulum 10B: Multilingual Large Language Models (LLMs)**
+
+Triangulum 10B is a collection of pretrained and instruction-tuned generative models, designed for multilingual applications. These models are trained using synthetic datasets based on long chains of thought, enabling them to perform complex reasoning tasks effectively.
+
+# **Key Features**
+
+- **Foundation Model**: Built upon LLaMA's autoregressive language model, leveraging an optimized transformer architecture for enhanced performance.
+
+- **Instruction Tuning**: Includes supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align model outputs with human preferences for helpfulness and safety.
+
+- **Multilingual Support**: Designed to handle multiple languages, ensuring broad applicability across diverse linguistic contexts.
+
+# **Training Approach**
+
+1. **Synthetic Datasets**: Utilizes long chain-of-thought synthetic data to enhance reasoning capabilities.
+2. **Supervised Fine-Tuning (SFT)**: Aligns the model to specific tasks through curated datasets.
+3. **Reinforcement Learning with Human Feedback (RLHF)**: Ensures the model adheres to human values and safety guidelines through iterative training processes.
+
+# **Use Cases**
+- Multilingual content generation
+- Question answering and dialogue systems
+- Text summarization and analysis
+- Translation and localization tasks
+
+# **Technical Details**
+Triangulum 10B employs a state-of-the-art autoregressive architecture inspired by LLaMA. The optimized transformer framework ensures both efficiency and scalability, making it suitable for a variety of use cases.
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+