From bdbd941486eba96809339245accbfec76bd75ce2 Mon Sep 17 00:00:00 2001
From: xxl <505279206@qq.com>
Date: Mon, 25 Nov 2024 10:10:59 +0800
Subject: [PATCH] first commit
---
LICENSE | 217 +
NOTICE | 13 +
README.md | 129 +-
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config.json | 28 +
configuration.json | 1 +
generation_config.json | 14 +
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model-00001-of-00004.safetensors | 3 +
model-00002-of-00004.safetensors | 3 +
model-00003-of-00004.safetensors | 3 +
model-00004-of-00004.safetensors | 3 +
model.safetensors.index.json | 346 +
tokenizer.json | 303288 ++++++++++++++++++++++++++++
tokenizer_config.json | 40 +
vocab.json | 1 +
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diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..52ccd3a
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+++ b/LICENSE
@@ -0,0 +1,217 @@
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\ No newline at end of file
diff --git a/NOTICE b/NOTICE
new file mode 100644
index 0000000..bee5325
--- /dev/null
+++ b/NOTICE
@@ -0,0 +1,13 @@
+Copyright (C) 2024 AIDC-AI
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+ http://www.apache.org/licenses/LICENSE-2.0
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+This model was trained based on the following model:
+Qwen2.5-7B-Instruct (https://huggingface.co/Qwen/Qwen2.5-7B-Instruct), license:(https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE, SPDX-License-identifier:Apache-2.0)
\ No newline at end of file
diff --git a/README.md b/README.md
index 7130169..fbc5640 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,128 @@
-# Marco-o1_a13714689447424000114709
+---
+license: apache-2.0
+library_name: transformers
+inference: false
+---
+
+ 
-Marco-o1
\ No newline at end of file
+
+
+# 🍓 Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
+
+
+
+
+
+
+
+
+
+
+
+
+
+⭐ _**MarcoPolo Team**_ ⭐
+
+[_**AI Business, Alibaba International Digital Commerce**_](https://aidc-ai.com)
+
+[**Github**](https://github.com/AIDC-AI/Marco-o1) 🤗 [**Hugging Face**](https://huggingface.co/AIDC-AI/Marco-o1) 📝 [**Paper**](https://arxiv.org/abs/2411.14405) 🧑💻 [**Model**](https://huggingface.co/AIDC-AI/Marco-o1) 🗂️ [**Data**](https://github.com/AIDC-AI/Marco-o1/tree/main/data) 📽️ [**Demo**](https://huggingface.co/AIDC-AI/Marco-o1)
+
+
+
+🎯 **Marco-o1** not only focuses on disciplines with standard answers, such as mathematics, physics, and coding—which are well-suited for reinforcement learning (RL)—but also places greater emphasis on **open-ended resolutions**. We aim to address the question: _"Can the o1 model effectively generalize to broader domains where clear standards are absent and rewards are challenging to quantify?"_
+
+Currently, Marco-o1 Large Language Model (LLM) is powered by _Chain-of-Thought (CoT) fine-tuning_, _Monte Carlo Tree Search (MCTS)_, _reflection mechanisms_, and _innovative reasoning strategies_—optimized for complex real-world problem-solving tasks.
+
+⚠️ **Limitations:** We would like to emphasize that this research work is inspired by OpenAI's o1 (from which the name is also derived). This work aims to explore potential approaches to shed light on the currently unclear technical roadmap for large reasoning models. Besides, our focus is on open-ended questions, and we have observed interesting phenomena in multilingual applications. However, we must acknowledge that the current model primarily exhibits o1-like reasoning characteristics and its performance still fall short of a fully realized "o1" model. This is not a one-time effort, and we remain committed to continuous optimization and ongoing improvement.
+
+
+
+## 🚀 Highlights
+Currently, our work is distinguished by the following highlights:
+
+- 🍀 Fine-Tuning with CoT Data: We develop Marco-o1-CoT by performing full-parameter fine-tuning on the base model using open-source CoT dataset combined with our self-developed synthetic data.
+- 🍀 Solution Space Expansion via MCTS: We integrate LLMs with MCTS (Marco-o1-MCTS), using the model's output confidence to guide the search and expand the solution space.
+- 🍀 Reasoning Action Strategy: We implement novel reasoning action strategies and a reflection mechanism (Marco-o1-MCTS Mini-Step), including exploring different action granularities within the MCTS framework and prompting the model to self-reflect, thereby significantly enhancing the model's ability to solve complex problems.
+- 🍀 Application in Translation Tasks: We are the first to apply Large Reasoning Models (LRM) to Machine Translation task, exploring inference time scaling laws in the multilingual and translation domain.
+
+OpenAI recently introduced the groundbreaking o1 model, renowned for its exceptional reasoning capabilities. This model has demonstrated outstanding performance on platforms such as AIME, CodeForces, surpassing other leading models. Inspired by this success, we aimed to push the boundaries of LLMs even further, enhancing their reasoning abilities to tackle complex, real-world challenges.
+
+🌍 Marco-o1 leverages advanced techniques like CoT fine-tuning, MCTS, and Reasoning Action Strategies to enhance its reasoning power. As shown in Figure 2, by fine-tuning Qwen2-7B-Instruct with a combination of the filtered Open-O1 CoT dataset, Marco-o1 CoT dataset, and Marco-o1 Instruction dataset, Marco-o1 improved its handling of complex tasks. MCTS allows exploration of multiple reasoning paths using confidence scores derived from softmax-applied log probabilities of the top-k alternative tokens, guiding the model to optimal solutions. Moreover, our reasoning action strategy involves varying the granularity of actions within steps and mini-steps to optimize search efficiency and accuracy.
+
+
+
+
Figure 2: The overview of Marco-o1.
+
+
+
Figure 3: The main results of Marco-o1.
+
+
+
Figure 4: The demostration of translation task using Marco-o1.
+
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