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- Edit this `README.md` markdown file to author your organization card.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Bitext
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+ [![Website](https://img.shields.io/badge/website-visit-2ea44f)](https://www.bitext.com/)
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+ ## LLM Training with High-Quality Datasets
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+ At Bitext, we specialize in creating high-quality datasets and providing training solutions for Large Language Models (LLMs). Our services cover pre-training, domain adaptation, and fine-tuning to ensure your AI systems perform across various industries.
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+ ### Key Services
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+ - **Pre-training:** Utilize our comprehensive datasets to build solid foundational models.
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+ - **Domain Adaptation:** Tailor LLMs to specific industry requirements, ensuring relevance and accuracy.
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+ - **Fine-tuning:** Enhance model performance with our finely curated datasets for more than 20 verticals.
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+ ## Hallucination-free Datasets for Effective Fine-tuning
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+ Bitext enhances LLM fine-tuning with Hybrid Datasets and Data-Centric LLM fine-tuning. Our hybrid approach combines the scale of synthetic text with the quality of manual curation, ensuring high-quality results.
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+ ### Key Features
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+ - **Extensive Contextual Variety:** Reflects wide-ranging interaction scenarios.
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+ - **Linguistic Diversity:** Tailored to various communication tones and styles.
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+ - **Realistic Noise Generation:** Incorporates common errors to enhance robustness.
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+ - **Constant Updates:** Keeps LLMs updated with current linguistic trends.
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+ ### List of Fine-Tuning LLM Verticals
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+ We fine-tune LLMs to deliver precise, industry-tailored results across various sectors, including automotive, academia, and healthcare. Our specialized datasets ensure your customer support systems interact effectively in diverse scenarios.
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+ [Explore our Datasets](https://www.bitext.com/training-datasets/)
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+ ## From General-Purpose Models to Specialized Enterprise GenAI Use Cases
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+ ### Domain Adaptation for Enterprise GenAI Use
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+ Verticalization is essential for deploying AI in the enterprise. For example, a Banking domain model will understand that "opening an account" refers to a bank account, not an e-commerce account. This disambiguation is crucial for accurate AI responses.
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+ ### Our Two-Step Approach
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+ 1. **Verticalize your favorite model(s) for a specific domain.** We've tested this with GPT, Mistral, and others for the Banking vertical.
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+ 2. **Customize this verticalized model to your enterprise use case(s)** with your own data.
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+ ### Advantages
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+ - **Efficient Execution:** Completed in weeks.
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+ - **Standard Hardware:** Requires typical hardware setups.
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+ - **Regular Tools:** Uses common fine-tuning tools.
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+ Bitext's pre-built models are based on proprietary NLG technology, free from hallucinations, PII, and bias.
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+ [Learn More](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)
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+ ## Contact Us
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+ For more information, visit [our website](https://www.bitext.com/) or reach out to us directly.
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+ Bitext provides innovative solutions to enhance LLM performance across various industries with our hybrid datasets and fine-tuning expertise.