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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Moxin is a family of fully open-source and reproducible LLMs
| Date | Stars |
|---|---|
| 2026-07-31 | 525 |
| 2026-08-06 | 525 |
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# Moxin LLM Moxin is a family of fully open-source and reproducible LLMs [](https://arxiv.org/abs/2412.06845v5) [](https://github.com/moxin-org/Moxin-LLM/blob/main/LICENSE) [](https://huggingface.co/moxin-org) --- ## Introduction Generative AI (GAI) offers unprecedented opportunities for research and innovation, but its commercialization has raised concerns about transparency, reproducibility, and safety. Many open GAI models lack the necessary components for full understanding and reproducibility, and some use restrictive licenses whilst claiming to be “open-source”. To address these concerns, we follow the [Model Openness Framework (MOF)](https://arxiv.org/pdf/2403.13784), a ranked classification system that rates machine learning models based on their completeness and openness, following principles of open science, open source, open data, and open access. By promoting transparency and reproducibility, the MOF combats “openwashing” practices and establishes completeness and openness as primary criteria alongside the core tenets of responsible AI. Wide adoption of the MOF will foster a more open AI ecosystem, benefiting research, innovation, and adoption of state-of-the-art models. In line with the MOF, we release our datasets used during training, the training scripts, and the trained models. ### Quick Start - [Usage Guide](inference) - Inference code with Pytorch. - [Quantization and Deployment](llamacpp) - Implementation and Inference using Llama.cpp quantized models. ### Documentation - [Pre-Training](train) - Complete training documentation - [Post-Training](finetune) - Post-Training with Tülu 3 and Reinforcement Learning with GRPO - [Evaluation](benchmark) - Benchmarking and evaluation ## Model Family Overview You can download our [Moxin-7B-Base](https://huggingface.co/moxin-org/moxin-llm-7b), [Moxin-7B-Instruct](https://huggingface.co/moxin-org/moxin-instruct-7b), [Moxin-7B-Reasoning](https://huggingface.co/moxin-org/moxin-reasoning-7b) and [Moxin-7B-VLM](https://huggingface.co/moxin-org/Moxin-7B-VLM) models. ### Base Model We test the performance of our base model with [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The evaluation results on common datasets are shown below. We test on AI2 Reasoning Challenge (25-shot), HellaSwag (10-shot), MMLU (5-shot), and Winogrande (5-shot). We release the Moxin-7B-Enhanced as our base model. We further finetune our base model on Tulu v2 to obtain our chat model. | Models | ARC-C | Hellaswag | MMLU | WinoGrade | Ave | |:----------------------:|:-----:|:---------:|:-----:|:---------:|:-----:| | Mistral-7B | 57.59 | 83.25 | 62.42 | 78.77 | 70.51 | | LLaMA 3.1-8B | 54.61 | 81.95 | 65.16 | 77.35 | 69.77 | | LLaMA 3-8B | 55.46 | 82.09 | 65.29 | 77.82 | 70.17 | | LLaMA 2-7B | 49.74 | 78.94 | 45.89 | 74.27 | 62.21 | | Qwen 2-7B | 57.68 | 80.76 | 70.42 | 77.43 | 71.57 | | Gemma-7b | 56.48 | 82.31 | 63.02 | 78.3 | 70.03 | | Internlm2.5-7b | 54.78 | 79.7 | 68.17 | 80.9 | 70.89 | | Baichuan2-7B | 47.87 | 73.89 | 54.13 | 70.8 | 61.67 | | Yi-1.5-9B | 58.36 | 80.36 | 69.54 | 77.53 | 71.48 | | Moxin-7B-Original | 53.75 | 75.46 | 59.43 | 70.32 | 64.74 | | Moxin-7B-Enhanced (Moxin-7B-Base)| 59.47 | 83.08 | 60.97 | 78.69 | 70.55 | We also test the zero shot performance on AI2 Reasoning Challenge (0-shot), AI2 Reasoning Easy (0-shot), HellaSwag (0-shot), PIQA (0-shot) and Winogrande (0-shot). The results are shown below. | Models | HellaSwag | WinoGra
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:bbfc7370ce42dc37, desc:reproducible