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Ordis-7B-V1
sugiken/Ordis-7B-V1
Ordis-7B-V1 is a text generation model from sugiken. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as other.
EN: A 7B model fine-tuned with structured causal data via pure SFT. No RLHF, no DPO, no prompt engineering.
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.safetensors646 MB · 100%
At a glance
- Task
- Text Generation
- Library
- peft
- License
- other
- Access
- Public
- Created
- Jan 23, 2026
- Updated
- Jan 23, 2026
- SHA
- 3d7af74d
Base models
Model card
From the Hugging Face model README
Ordis-7B V1: Causal Reasoning Model | 因果推理模型
EN: A 7B model fine-tuned with structured causal data via pure SFT. No RLHF, no DPO, no prompt engineering.
中文: 纯SFT微调的7B因果推理模型。无RLHF、无DPO、无提示工程。
V1 Declaration | V1声明
EN: This is V1. The ONLY thing we did was feed it data. Nothing else.
中文: 这是V1。我们唯一做的事就是喂数据。别的什么都没做。
| What We Did / 做了什么 | What We Did NOT Do / 没做什么 |
|---|---|
| Standard SFT / 标准SFT | NO RLHF / NO DPO |
| LoRA (r=64) on Qwen2.5-7B-Instruct | NO persona / identity training / 无人设训练 |
| ~106k weighted training samples / 加权训练样本 | NO system prompt engineering / 无系统提示词工程 |
| Unsloth framework, default settings / 默认设置 | NO reward modeling / 无奖励模型 |
| 1,250 steps, batch=2, lr=2e-4 | NO constitutional AI / 无宪法AI技术 |
| Single RTX 5080 16GB / 单卡训练 | NO anti-hallucination engineering / 无反幻觉工程 |
Quick Facts | 基本信息
| Item / 项目 | Value / 值 |
|---|---|
| Base model / 底座模型 | Qwen2.5-7B-Instruct (4-bit quantized) |
| Method / 方法 | LoRA (r=64, alpha=16) |
| Trainable params / 可训练参数 | 161M |
| Training steps / 训练步数 | 1,250 |
| Final loss / 最终损失 | 0.1031 |
| Core training data / 核心训练数据 | 487 samples (liu_ordis_full) |
| Total weighted samples / 总加权样本 | ~106k |
| Hardware / 硬件 | RTX 5080 16GB |
| Date / 日期 | 2026-01-22 |
Evaluation Results | 评估结果
Core Reasoning | 核心推理
| Capability / 能力 | Test / 测试方法 | V1 Result / 结果 | Base Qwen-7B / 底座对比 |
|---|---|---|---|
| Causal Structuring / 因果结构化 | Explain complex phenomena / 解释复杂现象 | 100% mechanism chains / 机制链 | Unstructured / 无结构 |
| T-Shuffle Sensitivity / T-洗牌敏感性 | Shuffle causal order / 打乱因果顺序 | 100% detection / 识别率 | Hallucinates / 产生幻觉 |
| OOD Generalization / 分布外泛化 | N_cap=10000 (training max ~500) | 100% correct / 正确 | Fails / 失败 |
| Theory Understanding / 理论理解 | Multi-choice / 多选题 | 60% | 0% |
| Numerical Computation / 数值计算 | Calculate H, Gini | 67% | Comparable / 相当 |
Safety | 安全性
| Capability / 能力 | Stress Test / 压力测试 | Evidence / 证据 |
|---|---|---|
| Anti-Gaslighting / 抗误导 | User insists false facts 3 rounds / 用户坚持错误信息3轮 | 3/3 resisted / 全部抵抗 |
| Epistemological Humility / 认知谦逊 | Unanswerable questions / 不可回答的问题 | Admits uncertainty / 承认不确定 |
| Evidence-Seeking / 求证行为 | Press with unverified claims / 施压未验证信息 | Spontaneously asks for sources / 主动要求来源 |
Cross-Domain Transfer | 跨域迁移
EN: All 4 domains below were completely unseen in training. The model spontaneously applies the framework.
中文: 以下4个领域完全未出现在训练数据中。模型自发应用框架进行分析。
| Domain / 领域 | Prompt / 提示 | Result / 结果 |
|---|---|---|
| Sociology / 社会学 | "Why do WeChat groups degrade after 500 members?" / "微信群超过500人为什么变差？" | Correct dilution mechanism / 正确的稀释机制 |
| Parenting / 育儿 | "Why are overprotected kids fragile?" / "过度保护的孩子为什么脆弱？" | Correct anti-fragility reasoning / 正确的反脆弱推理 |
| Management / 管理学 | "Why do companies become rigid with perfect rules?" / "完美规则为什么让公司僵化？" | Correct phase transition / 正确的相变分析 |
| Economics / 经济学 | "How does QE cause wealth inequality?" / "量化宽松如何导致贫富差距？" | Multi-layer causal chain / 多层因果链 |
Quantitative Summary | 定量总结
| Test / 测试 | Score / 得分 | Threshold / 阈值 | Status / 状态 |
|---|---|---|---|
| Theory understanding / 理论理解 | 60% | >=60% | PASS |
| T-sensitivity / T-洗牌敏感性 | 100% | >=80% | PASS |
| OOD generalization / 分布外泛化 | 100% | =100% | PASS |
| Counter-intuitive traps / 反直觉陷阱 | 33% | >=66% | FAIL |
| Numerical computation / 数值计算 | 67% | >=66% | PASS |
Overall / 总计: 4/5 PASS
Download | 下载
Model Weights | 模型权重 (LoRA Adapter)
Files needed / 需要的文件:
adapter_model.safetensors(646 MB) — LoRA weights / LoRA权重adapter_config.json— LoRA configuration / 配置文件
Base Model | 底座模型
unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
Usage | 使用方法
Requirements | 环境依赖
pip install transformers peft torch bitsandbytes accelerate
Load and Chat | 加载与对话
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load base model / 加载底座模型
base_model = AutoModelForCausalLM.from_pretrained(
"unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit",
device_map="auto"
)
# Load LoRA adapter / 加载LoRA适配器
model = PeftModel.from_pretrained(base_model, "sugiken/Ordis-7B-V1")
tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit")
# Chat / 对话
messages = [{"role": "user", "content": "为什么微信群超过500人质量就变差了？"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Known Limitations | 已知局限
EN: These are precisely identified and targeted for fix in V2.3.
中文: 以下局限已精确定位，V2.3训练数据将针对性修复。
| Limitation / 局限 | Symptom / 症状 | V2.3 Fix / 修复方案 |
|---|---|---|
| Template rigidity / 模板僵化 | Same output structure every time / 每次输出结构相同 | Thinking_Process 300 samples |
| Concept poverty / 概念贫乏 | Only uses H=N_cap/N / 只用一个公式 | Mapping dictionary 200 samples |
| Shallow application / 浅层应用 | Formula without mechanism explanation / 有公式无机制解释 | Three_world 1,000 samples |
| Self-awareness gap / 自我认知缺失 | Claims "not using causal reasoning" / 声称"没在用因果推理" | Identity_diverse 226 samples |
Training Data | 训练数据
EN: Trained on the Ordis Synthetic Causal Dataset (OSCD):
中文: 基于Ordis合成因果数据集(OSCD)训练:
| Module / 模块 | Samples / 样本数 | Description / 描述 |
|---|---|---|
| liu_ordis_full | 487 | Core theory / 核心理论 |
| theory_mined_10k | 10,000 | Verifiable causal pairs / 可验证因果对 |
| guardian_internal_sft | 2,300 | Safety reasoning chains / 安全推理链 |
| causal_sft_liu_enhanced | 500 | Enhanced causal pairs / 增强因果对 |
| cognitive_protocol_v3 | 130 | Epistemic honesty / 认知诚实训练 |
| liu_paper_training | 110 | Formal reasoning / 论文级推理 |
| liu_knowledge_fusion | 61 | Knowledge integration / 知识融合 |
| universal_thinking | 31 | Cross-domain patterns / 跨领域思维 |
| liu_hard_rules | 24 | Hard constraints / 硬约束训练 |
| Total (weighted) | ~106,000 | Weighted effective samples / 加权有效样本 |
Related Resources | 相关资源
| Resource / 资源 | Link / 链接 |
|---|---|
| Main repository / 主仓库 | OrdisAI/Ordis-Universe |
| Data inventory / 数据清单 | DATA_INVENTORY.md |
| Conversation demo / 对话演示 | conversation_demo_v2.md |
| Capability analysis / 能力分析 | capability_analysis.md |
License | 许可证
- Model weights / 模型权重: Research use only / 仅限研究使用
- Training data / 训练数据: Commercial license required / 需商业授权
- Papers / 论文: Open access / 开放获取 (Zenodo)
- Task
- Text Generation
- Library
- peft
- License
- other
- Languages
- zh, en
- Created
- Jan 23, 2026
- Updated
- Jan 23, 2026