中文翻译版在下面

🦙 Soren-Oracle-Chat-3B

🚀 Soren-Oracle-Chat-3B is an instruction-tuned dialogue model that has been comprehensively optimized and deeply fine-tuned on meta/Llama-3.2-3B-Instruct.

This project aims to achieve a generational leap in response quality, moving beyond traditional fine-tuning. It systematically enhances the model's performance, particularly in response formatting (Gestalt), length control, professional depth, and logical rigor. By integrating multiple high-quality Chinese and English instruction datasets, Soren-Oracle-Chat-3B is not only proficient in fluent Chinese conversation but also excels at presenting complex information in a human-friendly, highly structured manner, delivering professional-grade answers that are both deep and clear.

The ultimate goal is to create a "more expressive" 3B-level model, making it more outstanding and reliable in scenarios such as knowledge Q&A, content creation, and complex multi-turn dialogues.

This repository provides LoRA adapters and GGUF quantized versions for flexible deployment across various hardware environments.

✨Jackrong/Soren-Logos-3B is a GRPO-trained version of Soren-Oracle-Chat-3B produced after a set number of optimization steps.

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Model Enhancements and Strengths

Soren-Oracle-Chat-3B is not just a simple fine-tuning of the base model but a comprehensive optimization of response quality. This upgrade focuses on the following areas, significantly improving the model's overall capabilities and user experience:

Human-Friendly Formatting

• The model is now adept at using Markdown formatting (e.g., bullet points, bold, italics, code blocks) to organize its answers, making the presentation of complex information clearer, more structured, and easier to read.
• When step-by-step explanations or lists of key points are required, the model automatically employs ordered or unordered lists, greatly enhancing the readability of long-form answers.

Professionalism and Depth

• By incorporating training data rich in professional knowledge and chain-of-thought, the model delivers responses with greater depth and logical consistency when tackling specialized topics, rather than merely listing surface-level information.
• The language style of the responses is more rigorous and professional, with fewer colloquialisms and uncertain expressions, making it better suited for knowledge Q&A, report writing, and other professional contexts.

Optimal Response Length

• The model's ability to control response length has been optimized. It now generates answers of appropriate length based on the question's complexity. For simple questions, it provides concise and clear answers; for complex topics, it offers detailed yet non-redundant explanations.

Enhanced Instruction Following & Coherence

• The model's ability to understand and execute complex, multi-step instructions has been significantly improved.
• In multi-turn conversations, the model better maintains contextual coherence, resulting in smoother and more natural interactions.

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Below is a comparison of responses from the fine-tuned model and the base model on several questions, intuitively demonstrating the improvements in formatting, logic, and professionalism.

Model Capability Comparison

Question	Soren-Oracle-Chat-3B (Fine-tuned)	Official Llama-3.2-3B-Instruct
1. Can machines think?	Answer: Machines do not possess the ability to "think" or have "thoughts." 💡 Key Distinction: Machine vs. Machine Thinking: - Machine (Device): Lacks consciousness, subjective experience, etc. - Machine Thinking (or AI): Highly simulated and algorithm-based. 🧠 Why can't machines "think"?: - 1️⃣ Data-Driven: Models rely on training data, not subjective consciousness. - 2️⃣ Lack of Self-Awareness: Cannot generate subjective experiences or self-awareness. Comment: Uses paragraphs, lists, and emojis for a clearer structure and greater depth.	Answer: Currently, machines do not have the ability to think in the true sense. However, Machine Learning and Deep Learning can mimic human thinking in some aspects, making predictions and decisions, but they lack critical thinking and creativity. Comment: The answer is relatively plain and lacks a hierarchical structure.
2. Age Calculation Problem	Setup: Let's say Xiaoming is x years old, and his father is 4x years old. In 5 years: Xiaoming will be x + 5, and his father will be 4x + 5. Set up the equation: 4x + 5 = 3(x + 5) → Solve for x, which gives x = 10. Answer: ✅ Xiaoming is currently 10 years old, and his father is 40 years old. Comment: The reasoning steps are clear, and the result is correct.	Explanation: The original response attempted to set up an equation but incorrectly incorporated the "in 5 years" condition into the initial equality, leading to a calculation error. It produced incorrect forms like d = 4m + 5. Comment: Logical error, incorrect answer.
3. History of Apple Inc.	Answer: 🍎 Apple Inc. is a globally renowned innovation company. Early Years (1976–1980): Founding and First Products: Introduction to its founding and initial products; Entry into Education: Early strategies for the education market. Notable Milestones: Lists major timelines and products. Comment: Uses subheadings and bullet points, resulting in a clearer and more organized structure.	Answer: Apple Inc. has a rich history. Early Years (1976–1980): Introduction to the company's founding and first products; The Macintosh Era (1980–1996): Basic points like the release of the Macintosh in 1984. Comment: Presented as a simple list with more generalized information.
4. Explain "Neural Network"	Answer: Introduction and Core Concepts of Neural Networks. Definition and Historical Context: Introduction to its origins and development. Core Components: Neurons and Network Structure: Neuron, Layer, Connection Weights, etc. Working Principles and Architecture Types: Feedforward Networks, Convolutional Neural Networks, Recurrent Neural Networks, and their applications. Comment: A comprehensive and in-depth answer covering history, components, principles, architecture, and applications in a highly structured format.	Answer: A neural network is a machine learning model that mimics the brain's neural system. Basic Components: Composed of multiple "neurons" and a hierarchical structure. Working Principle: Data input, weights, and activation functions, etc. Comment: The explanation is relatively basic and generalized.

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Training Data

This model was fine-tuned on a carefully curated mixed dataset, which integrates the following four sources, totaling 86,448 samples (after length filtering):

• Jackrong/Chinese-Qwen3-235B-Thinking-2025: High-quality Chinese chain-of-thought distilled data to enhance the model's logical reasoning and Chinese expression abilities.
• Jackrong/Qwen3-235B-A22B-Instruct-2025: High-quality Chinese instruction-following dialogue distilled data to improve the model's ability to follow instructions in a Chinese context.
• facebook/natural_reasoning (Key Addition): An English dataset focused on improving general reasoning capabilities. See below for details.
• Infinity_Instruct_chat_qwen3_235B_Gen.jsonl: A private dialogue dataset containing diverse instructions to broaden the model's knowledge base and application scenarios.

Introduction to the Core Reasoning Dataset: facebook/natural_reasoning

To strengthen the model's underlying reasoning abilities, we specifically incorporated the facebook/natural_reasoning dataset. This is a large-scale, high-quality dataset released by Meta AI for general-purpose reasoning tasks.

• High Quality and Difficulty: The dataset contains challenging reasoning problems sourced from pre-training corpora like DCLM and FineMath, and generated through back-translation.
• Data Purity: All questions have undergone rigorous deduplication and decontamination processes to ensure no overlap with major reasoning benchmarks such as MATH, GPQA, and MMLU-Pro.
• Rich Answer Formats: The dataset provides not only reference answers extracted from original documents but also model-generated answers from Llama3.3-70B-Instruct for reference. In its public subset of 1.1 million examples, the answer types are diverse, with 50.42% being long-form answers and 21.58% short-form, ensuring data richness.
• Proven Scaling Effects: According to official research, training the Llama3.1-8B-Instruct model with the NaturalReasoning dataset shows superior performance scaling on multiple reasoning benchmarks like MATH, GPQA, and MMLU-Pro compared to using other datasets.

By introducing this dataset, the training of Soren-Oracle-Chat-3B goes beyond learning dialogue patterns; it reinforces its core logical reasoning engine, which is key to the model's ability to provide professional and in-depth answers.

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Actual Output Showcase:

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Intended Use & Limitations

Intended Use

This model is primarily intended for general-purpose Chinese and English dialogue, instruction following, and simple logical reasoning tasks. It is suitable as a foundation for chatbots, content creation assistants, or applications that require understanding and executing instructions.

Limitations

• Like all language models, this model may produce inaccurate, biased, or harmful content. Please conduct a thorough evaluation before use.
• The model's knowledge cutoff is December 2023, and it may not be aware of events that have occurred since then.
• Despite being trained on reasoning datasets, due to the inherent limitations of the base model, it may still perform poorly on extremely complex mathematical or logical reasoning problems.

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Acknowledgements

The creation of Soren-Oracle-Chat-3B` is built upon the outstanding prior work and collective wisdom of the open-source community. We express our sincerest gratitude to all contributors who provided the foundation, tools, and inspiration for this project.

Core Contributors

Base LLM: I used meta/Llama-3.2-3B-Instruct, developed by Meta, as the starting point for this model. Its excellent architecture and powerful base capabilities were key to the success of this project. I encourage users of this model to also acknowledge and cite the original contributors of the aforementioned base model and datasets. The open-source community thrives on sharing, and I hope Soren-Oracle-Chat-3B can be a part of that strength.

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⚠️ Disclaimer

Please note that the core objective of this fine-tuning was to optimize and enhance the model's capabilities in specific areas, but it is not a complete retraining.

The ultimate performance, knowledge boundary, and capability ceiling of this model are strictly limited by the inherent framework of its base model. Fine-tuning can improve performance in certain aspects but cannot overcome the fundamental limitations of the base model itself.

Therefore, the improvements brought about by fine-tuning are incremental and do not represent a qualitative leap. I advise users to independently cross-verify all critical information and to cautiously evaluate the model's output.

🦙 Soren-Oracle-Chat-3B

🚀 Soren-Oracle-Chat-3B 是一个在 meta/Llama-3.2-3B-Instruct 基础上进行全面优化和深度微调的指令对话模型。

本项目旨在实现回答能力的代际提升，超越传统微调，特别在回答的格式（Gestalt）、长度控制、专业深度和逻辑严谨性方面进行了系统性强化。通过融合多个高质量的中英文指令数据集，Soren-Oracle-Chat-3B 不仅精通流畅的中文对话，更擅长以人类友好的、高度结构化的方式呈现复杂信息，提供兼具深度与清晰度的专业级回答。

最终目标是打造一个“更会表达”的3B级模型，使其在知识问答、内容创作和多轮复杂对话等场景中表现得更为出色和可靠。

该仓库提供了 LoRA 适配器及 GGUF 量化版本，便于在各种硬件环境中灵活部署。

✨Jackrong/Soren-Logos-3B是Soren-Oracle-Chat-3B经过一定步数的GRPO的模型。

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模型提升与优势 (Model Enhancements and Strengths)

Soren-Oracle-Chat-3B 不仅仅是基座模型的简单微调，更是一次针对回答质量的全面优化。本次升级主要聚焦于以下几个方面，显著提升了模型的综合能力和用户体验：

人类友好格式 (Human-Friendly Formatting)

• 模型现在能娴熟地运用 Markdown 格式（如项目符号、粗体、斜体、代码块等）来组织回答，使得复杂信息的呈现更加清晰、结构化和易于阅读。
• 在需要分步解释或列举要点时，模型能自动采用有序或无序列表，极大改善了长篇回答的可读性。

专业性与深度 (Professionalism and Depth)

• 通过引入包含大量专业知识和思维链的训练数据，模型在处理专业领域问题时，能够提供更具深度和逻辑性的回答，而不仅仅是表面信息的罗列。
• 回答的语言风格更加严谨和专业，减少了口语化和不确定的表达，使其更适用于知识问答、报告撰写等场景。

理想的回答长度 (Optimal Response Length)

• 优化了模型的回答长度控制能力。现在，模型能够根据问题的复杂程度，生成长度适宜的回答。对于简单问题，它会给出简洁明了的答案；对于复杂问题，则能提供详尽而不冗余的解释。

指令遵循与对话连贯性 (Enhanced Instruction Following & Coherence)

• 全面提升了对复杂和多步骤指令的理解与执行能力。
• 在多轮对话中，模型能更好地保持上下文连贯性，使得交流更加顺畅自然。

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以下是微调后的模型与基座模型在几个问题上的回答对比，直观展示了模型在格式化、逻辑性和专业性上的提升。

模型能力对比展示

问题 (Question)	Soren-Oracle-Chat-3B (微调后)	官方 Llama-3.2-3B-Instruct
1. 机器能够思考吗？	回答：机器并非具有“思维”或“思考”的能力。 💡 核心区分：机器 vs 机器思考： - 机器（设备）：无意识、无主观体验等。 - 机器思考（或 AI）：高度模拟化、基于算法。 🧠 为什么机器无法“思维”？： - 1️⃣ 数据驱动：模型依赖训练数据而非主观意识。 - 2️⃣ 缺乏自我认知：无法产生主观体验或自我意识。 评注：使用段落、列表与 Emoji，结构清晰且深度更佳。	回答：目前，机器尚未具有 true 的思考能力。然而，机器学习（Machine Learning）和深度学习（Deep Learning）已经可以在某些方面模仿人类的思考，能够做出预测和决策，但缺乏批判性思维和创造力。 评注：回答较为平铺直叙，缺乏结构层次。
2. 年龄计算题	设定：设小明今年 x 岁，爸爸今年 4x 岁。 5 年后：小明 x + 5，爸爸 4x + 5。 列方程：4x + 5 = 3(x + 5) → 解得 x = 10。 答案：✅小明现在 10 岁，爸爸现在 40 岁。 评注：推理步骤清晰，结果正确。	说明：原回答尝试建立方程但在构建时把“5年后”的条件错误地并入初始等式，导致最终计算错误。示例出现 d = 4m + 5 等错误形式。 评注：逻辑错误，答案不正确。
3. 苹果公司的发展史	回答：🍎Apple Inc. 是全球知名创新公司。 Early Years (1976–1980)：Founding and First Products：介绍创立与最初产品；Entry into Education：早期教育市场策略。 Notable Milestones：列出重大时间点与产品。 评注：使用子标题与条目，结构更清晰、条理性更好。	回答：Apple Inc. 有丰富的发展史。**Early Years (1976–1980)：介绍公司创立及首批产品；The Macintosh Era (1980–1996)**：1984 年 Macintosh 发布等基本条目。 评注：以简单列表形式给出，信息较概括。
4. 讲解什么是“神经网络”	回答：神经网络（Neural Network）简介与核心概念。 定义与历史背景：介绍起源与发展脉络。 核心组成：神经元与网络结构：神经元（Neuron）、层（Layer）、连接权重等。 工作原理与架构类型：前馈网络、卷积神经网络、循环神经网络等，以及应用场景。 评注：回答全面深入，覆盖历史、组件、原理、架构与应用，结构化程度高。	回答：神经网络是一种模仿大脑神经系统的机器学习模型。基本组成：由多个“神经元”与层级结构构成。工作原理：数据输入、权重与激活函数等。 评注：解释相对基础与概括。

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训练数据 (Training Data)

本模型在一个精心构建的混合数据集上进行微调，该数据集整合了以下四个来源，总计 86,448 个样本（经过长度过滤后）：

• Jackrong/Chinese-Qwen3-235B-Thinking-2025 : 高质量的中文思维链蒸馏数据，用于提升模型的逻辑推理与中文表达能力。
• Jackrong/Qwen3-235B-A22B-Instruct-2025: 高质量的中文指令对话蒸馏数据，增强模型在中文语境下的指令遵循能力。
• facebook/natural_reasoning (重点引入): 。专注于提升通用推理能力的英文数据集，详见下文介绍。
• Infinity_Instruct_chat_qwen3_235B_Gen.jsonl: 一个私有的、包含多样化指令的对话数据集，用于拓宽模型的知识面和应用场景。

核心推理数据集介绍: facebook/natural_reasoning

为了强化模型的底层推理能力，我们特别引入了 facebook/natural_reasoning数据集。这是一个由 Meta AI 发布、用于通用推理任务的大规模、高质量数据集。

• 高质量与高难度: 数据集包含具有挑战性的推理问题，这些问题来源于 DCLM 和 FineMath 等预训练语料库，并通过反向翻译生成。
• 数据纯净度: 所有问题都经过了严格的去重和去污染处理，确保与 MATH、GPQA、MMLU-Pro 等主流推理基准测试集不存在重叠。
• 丰富的答案形式: 数据集不仅提供了从原始文档中提取的参考答案，还包含由 Llama3.3-70B-Instruct 生成的模型回答作为参考。在其 110 万的公开发布子集中，答案类型多样，其中 50.42% 为长答案，21.58% 为短答案，确保了训练数据的丰富性。
• 验证有效的扩展性 (Proven Scaling Effects): 根据官方研究，使用 NaturalReasoning 数据集训练 Llama3.1-8B-Instruct 模型，在 MATH、GPQA 和 MMLU-Pro 等多个推理基准上，表现出比使用其他数据集更优的性能扩展效果。

通过引入这个数据集，Soren-Oracle-Chat-3B 的训练不仅是学习对话模式，更是在强化其逻辑推理的核心引擎，这是模型能够提供专业、深度回答的关键。

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输出内容实际展示:

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预期用途与限制 (Intended Use & Limitations)

预期用途

本模型主要用于通用的中英文对话、指令遵循和简单的逻辑推理任务。它适合作为聊天机器人、内容创作助手或需要理解和执行指令的应用的基础。

限制

• 与所有语言模型一样，本模型可能产生不准确、有偏见或有害的内容。请在使用前进行充分的评估。
• 模型的知识截止日期为 2023 年 12 月，对于此后发生的事件可能不知情。
• 尽管在推理数据集上进行了训练，出于基座模型的能力限制，它在极其复杂的数学或逻辑推理问题上仍然表现不佳。

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致谢 (Acknowledgements)

Soren-Oracle-Chat-3B` 的诞生，是建立在众多杰出的前期工作和开源社区的集体智慧之上。我们对所有为本项目提供基础、工具和灵感的贡献者表示最诚挚的感谢。

核心贡献者 (Core Contributors)

基础模型 (Base LLM): 我采用了由 Meta 公司研发的 meta/Llama-3.2-3B-Instruct 作为模型的起点。其卓越的架构和强大的基础能力，是本项目成功的关键。我鼓励用户在使用本模型时，同样对上述基础模型和数据集的原始贡献者进行致谢和引用。开源社区因分享而繁荣，我希望 Soren-Oracle-Chat-3B 也能成为这份力量的一部分。

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⚠️声明

请注意，本次微调的核心目标是优化和增强模型在特定方面的能力，但并非一次完整的重新训练。

本模型的最终性能、知识边界和能力的上限，均严格受限于其基座模型的固有框架。微调能够改善模型在某些方面的表现，但无法突破基座模型自身存在的根本性限制。

因此，微调所带来的改进是增量式的，并不能带来质的飞跃。我建议用户在使用模型时，对所有关键信息进行独立的交叉验证，并审慎评估其输出结果。