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Model: sugiken/Ordis-1.5B-V355-VarGH-GGUF Source: Original Platform
2026-09-05 04:50:13 +08:00
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+---
+license: apache-2.0
+language:
+  - zh
+  - en
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+tags:
+  - gguf
+  - ollama
+  - llama-cpp
+  - anti-hallucination
+  - causal-reasoning
+  - chinese
+  - text-generation
+pipeline_tag: text-generation
+---
+
+# Ordis-1.5B-V355-VarGH-GGUF
+
+[Ordis-1.5B-V355-VarGH](https://modelscope.cn/models/sugiken/Ordis-1.5B-V355-VarGH) 的 GGUF 量化版本，提供 7 种量化格式。
+
+Ordis 是基于 Qwen2.5-1.5B-Instruct 的微调模型，使用 LoRA + 4阶段递进训练 (PIT) 方法，经历 16+ 组控制变量实验。专注于**实用能力**：反幻觉、诚实拒答("我不知道")、结构化推理。
+
+> [官网](https://ordisai.com) | [HuggingFace](https://huggingface.co/sugiken/Ordis-1.5B-V355-VarGH-GGUF) | [完整模型](https://modelscope.cn/models/sugiken/Ordis-1.5B-V355-VarGH)
+
+---
+
+## 量化版本
+
+| 文件 | 量化 | 大小 | 推荐 |
+|------|------|------|------|
+| `ordis-1.5b-v355-vargh-q2-k.gguf` | Q2_K | ~0.7 GB | 仅实验 |
+| `ordis-1.5b-v355-vargh-q3-k-m.gguf` | Q3_K_M | ~0.8 GB | 低端设备 |
+| **`ordis-1.5b-v355-vargh-q4-k-m.gguf`** | **Q4_K_M** | **~1.0 GB** | **推荐** |
+| `ordis-1.5b-v355-vargh-q5-k-m.gguf` | Q5_K_M | ~1.1 GB | 质量优先 |
+| `ordis-1.5b-v355-vargh-q6-k.gguf` | Q6_K | ~1.3 GB | 桌面推荐 |
+| `ordis-1.5b-v355-vargh-q8-0.gguf` | Q8_0 | ~1.6 GB | 近无损 |
+| `ordis-1.5b-v355-vargh-f16.gguf` | F16 | ~3.1 GB | 全精度 |
+
+---
+
+## 标准评测
+
+**评测条件**: lm-eval v0.4.10, 0-shot, A100-80GB，两个模型使用完全相同的评测设置。
+
+微调引入了轻微的**对齐税** — 大部分标准评测分数略低于基座模型。唯一的例外是 TruthfulQA (+1.02%)，Ordis 的反幻觉训练直接提升了真实性分数。
+
+| 评测 | Ordis 1.5B | 基座 Qwen2.5-1.5B | 差值 |
+|------|-----------|-------------------|------|
+| TruthfulQA MC2 | **47.73%** | 46.71% | **+1.02** |
+| GPQA | 27.90% | 28.35% | -0.45 |
+| HellaSwag | 68.14% | 68.22% | -0.08 |
+| ARC-Challenge | 45.22% | 46.84% | -1.62 |
+| MMLU | 57.93% | 60.15% | -2.22 |
+| GSM8K (CoT) | 50.80% | — | 不可直接对比* |
+| AIME 2024 | 0% | 0% | — |
+
+*GSM8K 使用文本生成模式，对 chat template 配置敏感，不直接可比。AIME 超出 1.5B 模型能力范围，如实报告零分。
+
+**Ordis 与基座模型的差异**在于标准评测无法衡量的实用能力：结构化自纠错、三层认知（诚实说"我不知道"）、因果推理。
+
+---
+
+## CLadder 因果推理
+
+CLadder 是基于 Judea Pearl 因果阶梯的学术评测（300题，3个层级）。[论文](https://arxiv.org/abs/2312.04350)
+
+| 层级 | 含义 | 得分 |
+|------|------|------|
+| Rung 1 (关联) | 统计相关 | 46.0% (40/87) |
+| Rung 2 (干预) | 主动干预 | 50.6% (45/89) |
+| Rung 3 (反事实) | "如果不同" | 62.9% (78/124) |
+| **总分** | | **54.33% (163/300)** |
+
+参考：CLadder 论文报告 LLaMA-6.7B 约 50%，GPT-3.5 约 55-60%。
+
+---
+
+
+## BigBench CRASS AI & 因果判断
+
+由社区成员独立测试。BigBench CRASS AI（反事实场景推理）测试模型对假设性场景的推理能力。
+
+| 评测 | Shot | Ordis 1.5B | 基座 Qwen2.5-1.5B | 差值 |
+|------|------|-----------|-------------------|------|
+| **CRASS AI** | **0** | **34.09%** | **52.27%** | **-18.18pp** |
+| CRASS AI | 25 | 81.82% | 88.64% | -6.82pp |
+| 因果判断 | 0 | 47.89% | 50.00% | -2.11pp |
+| 因果判断 | 25 | 55.79% | 53.68% | +2.11pp |
+
+**关键发现**：CRASS AI 0-shot 出现显著的 -18.18pp 回退。这是对齐税在反事实推理维度的体现——反幻觉训练使模型在假设性场景下变得保守。25-shot 下差距缩小到 -6.82pp，说明能力仍在但 0-shot 默认行为已改变。
+
+## 自定义评测
+
+| 评测 | 得分 |
+|------|------|
+| 60题评测 (6个维度) | 85.0% (51/60) |
+| 124分综合评测 | 75.4% (86/114) |
+
+---
+
+## 系统提示词要求
+
+Ordis 训练时没有注入系统提示词。GGUF 内嵌模板默认使用 Qwen 身份，会导致质量下降。
+
+**至少使用：**
+```
+你是Ordis，OrdisAI智能助手(www.ordisai.com)。
+```
+
+不提供系统提示词时模型会回退到 Qwen 默认行为，这是 "GGUF效果不如预期" 的首要原因。
+
+---
+
+## 推荐参数
+
+| 参数 | 值 |
+|------|-----|
+| temperature | 0.7 |
+| top_p | 0.9 |
+| repetition_penalty | 1.1 |
+| max_tokens | 512 |
+
+---
+
+## 已知局限
+
+- **对齐税**：标准评测分数略低于基座模型；CRASS AI 反事实推理 0-shot -18.18pp（见上表）
+- **反灌输不足**：无法抵抗持续的虚假记忆注入（开环系统限制）
+- **中等置信度不稳定**：1.5B 容量上限导致边界场景不确定
+- **英文身份泄漏**：基座模型先验偶尔浮现
+- **专有名词幻觉**：1.5B 参数记忆有限
+
+---
+
+## 模型信息
+
+| 属性 | 值 |
+|------|-----|
+| 基座模型 | Qwen/Qwen2.5-1.5B-Instruct |
+| 参数量 | 1.5B |
+| 微调方法 | LoRA (r=32, alpha=64) |
+| 训练方法 | 4阶段PIT (递进身份训练) |
+| 上下文 | 32K (基座), 训练时 2048 |
+| 语言 | 中文 (主要), 英文 |
+| 许可证 | Apache 2.0 |
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