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README.md

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+Moxin 7B: A Fully Open-Source 7B Language Model with Unprecedented Transparency
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+We’re thrilled to unveil Moxin 7B, a new milestone in open large language model (LLM) development — designed to push the boundaries of performance and openness.
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+In an era where many "open" LLMs lack true transparency (e.g., missing training code, data, or restrictive licenses), Moxin 7B sets a new gold standard by committing to full disclosure and reproducibility. 
+Developed under the Model Openness Framework (MOF), Moxin 7B achieves the top classification level of Open Science, thanks to:
+
+**What we’ve open-sourced**:
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+- Pre-training code, data, and Moxin Base model.
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++ Post-training code, data, and Moxin Instruct model.
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++ RL code with GRPO, data and Moxin Reasoning model.
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+**Performance Highlights**:
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++ Zero-shot / Few-shot: Outperforms Mistral, Qwen, and LLaMA on tasks like HellaSwag, ARC, MMLU, and PIQA
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++ Reasoning: Moxin-Reasoning-7B achieves superior performance on MATH-500, AMC, and OlympiadBench — proving reinforcement learning can work for small 7B models
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++ Training cost: ~$160K for full pretraining — efficient and reproducible at scale
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+**Post-training Frameworks**:
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++ SFT and DPO with Tülu 3
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++ CoT-enhanced reasoning with GRPO via DeepScaleR
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+**Get the models and code**:
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++ Base model: Moxin-LLM-7B
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++ Instruction model: Moxin-Instruct-7B
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++ Reasoning model: Moxin-Reasoning-7B
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++ Code & docs: github.com/moxin-org/Moxin-LLM
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++ Arxiv paper: https://arxiv.org/abs/2412.06845
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+We believe this is a step toward a more transparent, reproducible, and innovation-friendly AI ecosystem — especially for researchers, developers, and startups looking to build upon a robust, open foundation.
+Let’s build open AI the right way.
+