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README.md
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# Bitext
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[](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.
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