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# 🤖
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Welcome to **
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## 🎯 Our Mission
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To push the boundaries of LLM inference through:
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- 🧠 **Fine-tuning on high-quality, domain-specific datasets**
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- 🛠️ **Post-processing and validation pipelines for output accuracy**
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- 🔍 **Benchmarking tools to measure and improve factual precision**
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- 🧪 **Instruction tuning, CoT reasoning, and RAG integration**
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## 🛠️ Projects
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| Project | Description | Model Card |
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| `AccuLLM-7B` | A fine-tuned LLaMA2 model focused on high factual recall in QA tasks | [🔗 View on Hugging Face](https://huggingface.co/your-org/AccuLLM-7B) |
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| `CoT-Refine` | Chain-of-Thought enhanced reasoning with accuracy post-verification | [🔗 View on Hugging Face](https://huggingface.co/your-org/CoT-Refine) |
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| `MedFact-LLM` | Medical reasoning model trained for low hallucination clinical answers | [🔗 View on Hugging Face](https://huggingface.co/your-org/MedFact-LLM) |
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## 📬 Stay Connected
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- 🤝 Collaborations: Open to partnerships and contributions
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- 📫 Contact: precisionllm@huggingface.co
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- 🌐 Website: Coming Soon!
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> “Better reasoning starts with better accuracy.” — *PrecisionLLM*
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🔗 **Follow us on Hugging Face:** [
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# 🤖 QuantumInk: Enhancing Inference Accuracy in Large Language Models
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Welcome to **QuantumInk**, a research-driven organization committed to **maximizing the inference accuracy of large language models (LLMs)**. We build fine-tuned, high-performance models and tooling designed to reduce hallucinations, increase factual correctness, and optimize domain-specific understanding.
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🔗 **Follow us on Hugging Face:** [QuantumInk](https://huggingface.co/QuantumInk)
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