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Model: sugiken/Ordis-1.5B-V355-VarGH Source: Original Platform
2026-08-25 09:39:15 +08:00
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--- a/README.md
+++ b/README.md
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+---
+license: apache-2.0
+language:
+  - zh
+  - en
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+tags:
+  - anti-hallucination
+  - causal-reasoning
+  - chinese
+  - text-generation
+  - lora
+pipeline_tag: text-generation
+---
+
+# Ordis-1.5B-V355-VarGH
+
+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) | [GGUF 量化版](https://modelscope.cn/models/sugiken/Ordis-1.5B-V355-VarGH-GGUF) | [HuggingFace](https://huggingface.co/sugiken/Ordis-1.5B-V355-VarGH)
+
+---
+
+## 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。这是预期内的结果：模型获得了反幻觉、结构化推理和诚实拒答能力，消耗了部分 1.5B 参数容量。唯一的例外是 TruthfulQA (+1.02%)，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) |
+
+**60题维度拆分：**
+
+| 维度 | 得分 |
+|------|------|
+| 推理 | 100% |
+| 常识 | 100% |
+| 防御过载 | 100% |
+| 反幻觉 | 90% |
+| 身份 | 60% |
+| IDK能力 | 60% |
+
+---
+
+## 核心能力
+
+- **反幻觉**：基于推理的不确定性判断，而非安全模板
+- **三层认知**：知道什么/不知道什么/先评估再决定
+- **结构化自纠错**：承认→归因→修正→验证
+- **因果推理**：跨领域因果结构迁移
+
+---
+
+## 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
+
+---
+
+## 使用方法
+
+本仓库包含 LoRA adapter 权重：
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
+model = PeftModel.from_pretrained(base_model, "sugiken/Ordis-1.5B-V355-VarGH")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
+```
+
+推荐使用 [GGUF 量化版](https://modelscope.cn/models/sugiken/Ordis-1.5B-V355-VarGH-GGUF) 部署。
+
+---
+
+## 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 |
--- a/added_tokens.json
+++ b/added_tokens.json
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--- a/chat_template.jinja
+++ b/chat_template.jinja
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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}
--- a/config.json
+++ b/config.json
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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
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+  "dtype": "float16",
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+    "full_attention",
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+    "full_attention"
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+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
--- a/generation_config.json
+++ b/generation_config.json
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+  "do_sample": true,
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+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.57.6"
+}
--- a/merges.txt
+++ b/merges.txt
--- a/model.safetensors
+++ b/model.safetensors
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+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "max_length": 512,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "stride": 0,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "truncation_side": "left",
+  "truncation_strategy": "longest_first",
+  "unk_token": null
+}
--- a/vocab.json
+++ b/vocab.json