《开源大模型食用指南》针对中国宝宝量身打造的基于Linux环境快速微调(全参数/Lora)、部署国内外开源大模型(LLM)/多模态大模型(MLLM)教程
This report presents the forensic synthetic code analysis of datawhalechina/self-llm, a Jupyter Notebook project with 31,276 GitHub stars. SynthScan v2.0 examined 100,364 lines of code across 375 source files, recording 257 pattern matches distributed across 14 syntactic categories. The overall adjusted score of 4.3 places this repository in the Likely human-written band.
The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).
This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.
Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.
This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.
The scanner identified 257 distinct pattern matches across 14 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.
Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | models/ChatGLM/05-ChatGLM3-6B接入LangChain搭建知识库助手.md | 0 | 使用以下上下文来回答最后的问题。如果你不知道答案,就说你不知道,不要试图编造答案。尽量使答案简明扼要。总是在回答的最后说“谢谢你的提问!”。 {context} 问题: {question} 有用的回答: | STRING |
| HIGH | models/InternLM/06-InternLM接入LangChain搭建知识库助手.md | 0 | 使用以下上下文来回答最后的问题。如果你不知道答案,就说你不知道,不要试图编造答案。尽量使答案简明扼要。总是在回答的最后说“谢谢你的提问!”。 {context} 问题: {question} 有用的回答: | STRING |
| HIGH | models/Atom/03-Atom-7B-Chat 接入langchain搭建知识库助手.md | 0 | 使用以下上下文来回答最后的问题。如果你不知道答案,就说你不知道,不要试图编造答案。尽量使答案简明扼要。总是在回答的最后说“谢谢你的提问!”。 {context} 问题: {question} 有用的回答: | STRING |
| HIGH | models/Qwen/07-Qwen-7B-Chat 接入langchain搭建知识库助手.md | 0 | 使用以下上下文来回答最后的问题。如果你不知道答案,就说你不知道,不要试图编造答案。尽量使答案简明扼要。总是在回答的最后说“谢谢你的提问!”。 {context} 问题: {question} 有用的回答: | STRING |
| HIGH | models/Yi/02-Yi-6B-Chat 接入langchain搭建知识库助手.md | 0 | 使用以下上下文来回答最后的问题。如果你不知道答案,就说你不知道,不要试图编造答案。尽量使答案简明扼要。总是在回答的最后说“谢谢你的提问!”。 {context} 问题: {question} 有用的回答: | STRING |
| HIGH | models/ChatGLM/05-ChatGLM3-6B接入LangChain搭建知识库助手.md | 0 | 使用以下上下文来回答最后的问题。如果你不知道答案,就说你不知道,不要试图编造答 案。尽量使答案简明扼要。总是在回答的最后说“谢谢你的提问!”。 {context} 问题: {question} 有用的回答: | STRING |
| HIGH | …ChatGLM/05-ChatGLM3-6B接入LangChain搭建知识库助手/run_gradio.py | 0 | 使用以下上下文来回答最后的问题。如果你不知道答案,就说你不知道,不要试图编造答 案。尽量使答案简明扼要。总是在回答的最后说“谢谢你的提问!”。 {context} 问题: {question} 有用的回答: | STRING |
| HIGH | models/InternLM/06-InternLM接入LangChain搭建知识库助手.md | 0 | 使用以下上下文来回答最后的问题。如果你不知道答案,就说你不知道,不要试图编造答 案。尽量使答案简明扼要。总是在回答的最后说“谢谢你的提问!”。 {context} 问题: {question} 有用的回答: | STRING |
| HIGH | …s/InternLM/06-InternLM接入LangChain搭建知识库助手/run_gradio.py | 0 | 使用以下上下文来回答最后的问题。如果你不知道答案,就说你不知道,不要试图编造答 案。尽量使答案简明扼要。总是在回答的最后说“谢谢你的提问!”。 {context} 问题: {question} 有用的回答: | STRING |
| HIGH | models/Atom/03-Atom-7B-Chat 接入langchain搭建知识库助手.md | 0 | 使用以下上下文来回答最后的问题。如果你不知道答案,就说你不知道,不要试图编造答 案。尽量使答案简明扼要。总是在回答的最后说“谢谢你的提问!”。 {context} 问题: {question} 有用的回答: | STRING |
| HIGH | models/Qwen/07-Qwen-7B-Chat 接入langchain搭建知识库助手.md | 0 | 使用以下上下文来回答最后的问题。如果你不知道答案,就说你不知道,不要试图编造答 案。尽量使答案简明扼要。总是在回答的最后说“谢谢你的提问!”。 {context} 问题: {question} 有用的回答: | STRING |
| HIGH | …/Qwen/07-Qwen-7B-Chat 接入langchain搭建知识库助手/run_gradio.py | 0 | 使用以下上下文来回答最后的问题。如果你不知道答案,就说你不知道,不要试图编造答 案。尽量使答案简明扼要。总是在回答的最后说“谢谢你的提问!”。 {context} 问题: {question} 有用的回答: | STRING |
| HIGH | models/ChatGLM/05-ChatGLM3-6B接入LangChain搭建知识库助手.md | 0 | 提醒:<br> 1. 初始化数据库时间可能较长,请耐心等待。 2. 使用中如果出现异常,将会在文本输入框进行展示,请不要惊慌。 <br> | STRING |
| HIGH | …ChatGLM/05-ChatGLM3-6B接入LangChain搭建知识库助手/run_gradio.py | 0 | 提醒:<br> 1. 初始化数据库时间可能较长,请耐心等待。 2. 使用中如果出现异常,将会在文本输入框进行展示,请不要惊慌。 <br> | STRING |
| HIGH | models/InternLM/06-InternLM接入LangChain搭建知识库助手.md | 0 | 提醒:<br> 1. 初始化数据库时间可能较长,请耐心等待。 2. 使用中如果出现异常,将会在文本输入框进行展示,请不要惊慌。 <br> | STRING |
| HIGH | …s/InternLM/06-InternLM接入LangChain搭建知识库助手/run_gradio.py | 0 | 提醒:<br> 1. 初始化数据库时间可能较长,请耐心等待。 2. 使用中如果出现异常,将会在文本输入框进行展示,请不要惊慌。 <br> | STRING |
| HIGH | models/Atom/03-Atom-7B-Chat 接入langchain搭建知识库助手.md | 0 | 提醒:<br> 1. 初始化数据库时间可能较长,请耐心等待。 2. 使用中如果出现异常,将会在文本输入框进行展示,请不要惊慌。 <br> | STRING |
| HIGH | …/Atom/03-Atom-7B-Chat 接入langchain搭建知识库助手/run_gradio.py | 0 | 提醒:<br> 1. 初始化数据库时间可能较长,请耐心等待。 2. 使用中如果出现异常,将会在文本输入框进行展示,请不要惊慌。 <br> | STRING |
| HIGH | models/Qwen/07-Qwen-7B-Chat 接入langchain搭建知识库助手.md | 0 | 提醒:<br> 1. 初始化数据库时间可能较长,请耐心等待。 2. 使用中如果出现异常,将会在文本输入框进行展示,请不要惊慌。 <br> | STRING |
| HIGH | …/Qwen/07-Qwen-7B-Chat 接入langchain搭建知识库助手/run_gradio.py | 0 | 提醒:<br> 1. 初始化数据库时间可能较长,请耐心等待。 2. 使用中如果出现异常,将会在文本输入框进行展示,请不要惊慌。 <br> | STRING |
| HIGH | models/Gemma3/04-Gemma3-4b evalscope智商情商评测.md | 0 | 以下为多个ai服务的api端点地址,用于配置任务: - siliconflow: https://api.siliconflow.cn/v1/chat/completions - dashscope: https://dashscope.a | STRING |
| HIGH | models/Gemma4/04-Gemma4-E4B-it evalscope智商情商评测.md | 0 | 以下为多个ai服务的api端点地址,用于配置任务: - siliconflow: https://api.siliconflow.cn/v1/chat/completions - dashscope: https://dashscope.a | STRING |
| HIGH | models/Qwen3/04-Qwen3-8B EvalScope智商情商评测.md | 0 | 以下为多个ai服务的api端点地址,用于配置任务: - siliconflow: https://api.siliconflow.cn/v1/chat/completions - dashscope: https://dashscope.a | STRING |
| HIGH | …3B-Instruct/04-Hunyuan-A13B-Instruct EvalScope 并发测试.md | 0 | 以下为多个ai服务的api端点地址,用于配置任务: - siliconflow: https://api.siliconflow.cn/v1/chat/completions - dashscope: https://dashscope.a | STRING |
| HIGH | models/Gemma3/6-gemma3-4B-itGRPO微调及通过swanlab可视化.md | 0 | you are given a problem. think about the problem and provide your working out. place it between {reasoning_start} and {r | STRING |
| HIGH | models/Gemma4/6-gemma4-E4B-itGRPO微调及通过swanlab可视化.md | 0 | you are given a problem. think about the problem and provide your working out. place it between {reasoning_start} and {r | STRING |
| HIGH | models/Qwen3/10-Qwen3-8B GRPO微调及通过swanlab可视化.md | 0 | you are given a problem. think about the problem and provide your working out. place it between {reasoning_start} and {r | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | utils.py | 124 | print(f"Error: {data['message']}") | CODE |
| LOW | models_amd/gemma3/2-gemma3-4b-it 模型服务部署.md | 86 | except Exception as e: | CODE |
| LOW | models_amd/qwen3/1-Qwen3-8B-AMD部署调用.md | 86 | except Exception as e: | CODE |
| LOW | models_amd/qwen3/1-Qwen3-8B-AMD部署调用.md | 196 | except Exception as e: | CODE |
| LOW | models_amd/qwen3/1-Qwen3-8B-AMD部署调用.md | 253 | except Exception as e: | CODE |
| LOW | models_amd/qwen3/1-Qwen3-8B-AMD部署调用.md | 266 | except Exception as e: | CODE |
| LOW | models_amd/qwen3/1-Qwen3-8B-AMD部署调用.md | 287 | except Exception as e: | CODE |
| LOW | models_amd/qwen3/1-Qwen3-8B-AMD部署调用.md | 362 | except Exception as e: | CODE |
| LOW | models/Llama4/01-Llama4-对话助手/01-Llama4-对话助手.md | 277 | except Exception as e: | CODE |
| LOW | models/Llama4/01-Llama4-对话助手/01-Llama4-对话助手.md | 291 | except Exception as e: | CODE |
| MEDIUM | models/Llama4/01-Llama4-对话助手/01-Llama4-对话助手.md | 217 | def generate(): | CODE |
| MEDIUM | models/Llama4/01-Llama4-对话助手/01-Llama4-对话助手.md | 281 | def clear_history(): | CODE |
| LOW | models/Llama4/01-Llama4-对话助手/app/app.py | 129 | except Exception as e: | CODE |
| LOW | models/Llama4/01-Llama4-对话助手/app/app.py | 143 | except Exception as e: | CODE |
| MEDIUM | models/Llama4/01-Llama4-对话助手/app/app.py | 69 | def generate(): | CODE |
| MEDIUM | models/Llama4/01-Llama4-对话助手/app/app.py | 133 | def clear_history(): | CODE |
| LOW | models/Qwen3-VL/02-Qwen3-VL-4B-Instruct FastApi 部署调用.md | 188 | except Exception as e: | CODE |
| LOW | models/Qwen3-VL/02-Qwen3-VL-4B-Instruct FastApi 部署调用.md | 255 | except Exception as e: | CODE |
| LOW | models/Qwen3-VL/02-Qwen3-VL-4B-Instruct FastApi 部署调用.md | 294 | except Exception as e: | CODE |
| LOW | models/Qwen3-VL/02-Qwen3-VL-4B-Instruct FastApi 部署调用.md | 345 | except Exception as e: | CODE |
| LOW | models/Qwen3-VL/02-Qwen3-VL-4B-Instruct FastApi 部署调用.md | 535 | except Exception as e: | CODE |
| LOW | models/Qwen3-VL/02-Qwen3-VL-4B-Instruct FastApi 部署调用.md | 599 | except Exception as e: | CODE |
| LOW | models/Qwen3-VL/02-Qwen3-VL-4B-Instruct FastApi 部署调用.md | 650 | except Exception as e: | CODE |
| LOW | …VL/05-Qwen3-VL-4B-Instruct Lora 可视化微调案例 - LaTexOCR.md | 725 | except Exception: | CODE |
| LOW | …VL/05-Qwen3-VL-4B-Instruct Lora 可视化微调案例 - LaTexOCR.md | 824 | except Exception: | CODE |
| LOW | …VL/05-Qwen3-VL-4B-Instruct Lora 可视化微调案例 - LaTexOCR.md | 836 | except Exception: | CODE |
| LOW | …VL/05-Qwen3-VL-4B-Instruct Lora 可视化微调案例 - LaTexOCR.md | 838 | except Exception as e: | CODE |
| LOW | …n3-VL/Qwen3-VL-4B-Instruct Lora 可视化微调案例 - LaTexOCR.md | 706 | except Exception: | CODE |
| LOW | …n3-VL/Qwen3-VL-4B-Instruct Lora 可视化微调案例 - LaTexOCR.md | 805 | except Exception: | CODE |
| LOW | …n3-VL/Qwen3-VL-4B-Instruct Lora 可视化微调案例 - LaTexOCR.md | 817 | except Exception: | CODE |
| LOW | …n3-VL/Qwen3-VL-4B-Instruct Lora 可视化微调案例 - LaTexOCR.md | 819 | except Exception as e: | CODE |
| LOW | …L-4B-Instruct FastApi 参考代码/api_server_qwen3vl_video.py | 126 | except Exception as e: | CODE |
| LOW | …1-Qwen3-VL-4B-Instruct FastApi 参考代码/test_simple_api.py | 25 | except Exception as e: | CODE |
| LOW | …1-Qwen3-VL-4B-Instruct FastApi 参考代码/test_simple_api.py | 64 | except Exception as e: | CODE |
| LOW | …1-Qwen3-VL-4B-Instruct FastApi 参考代码/test_simple_api.py | 115 | except Exception as e: | CODE |
| LOW | …01-Qwen3-VL-4B-Instruct FastApi 参考代码/test_video_api.py | 28 | except Exception as e: | CODE |
| LOW | …01-Qwen3-VL-4B-Instruct FastApi 参考代码/test_video_api.py | 79 | except Exception as e: | CODE |
| LOW | …-4B-Instruct FastApi 参考代码/api_server_qwen3vl_simple.py | 125 | except Exception as e: | CODE |
| LOW | …uct Lora 可视化微调案例 - LaTexOCR/compare_qwen3_vl_infer.py | 63 | except Exception: | CODE |
| LOW | …uct Lora 可视化微调案例 - LaTexOCR/compare_qwen3_vl_infer.py | 162 | except Exception: | CODE |
| LOW | …uct Lora 可视化微调案例 - LaTexOCR/compare_qwen3_vl_infer.py | 174 | except Exception: | CODE |
| LOW | …uct Lora 可视化微调案例 - LaTexOCR/compare_qwen3_vl_infer.py | 176 | except Exception as e: | CODE |
| LOW | models/gpt-oss/4-gpt-oss-20b Lora 微调及 SwanLab 可视化记录.md | 245 | except Exception as e: | CODE |
| LOW | models/gpt-oss/3-gpt-oss-20b lmstudio 本地部署调用.md | 130 | except Exception as exc: | CODE |
| LOW | models/ChatGLM/05-ChatGLM3-6B接入LangChain搭建知识库助手.md | 398 | except Exception as e: | CODE |
| LOW | models/ChatGLM/Extra-ChatGLM3-6B-Pipeline.md | 341 | except Exception as e: | CODE |
| LOW | models/ChatGLM/Extra-ChatGLM3-6B-Pipeline.md | 404 | except Exception as e: | CODE |
| LOW | models/ChatGLM/Extra-ChatGLM3-6B-Pipeline.md | 457 | except Exception as e: | CODE |
| LOW | models/ChatGLM/Extra-ChatGLM3-6B-Pipeline.md | 491 | except Exception as e: | CODE |
| LOW | …ChatGLM/05-ChatGLM3-6B接入LangChain搭建知识库助手/run_gradio.py | 71 | except Exception as e: | CODE |
| LOW | models/InternLM/06-InternLM接入LangChain搭建知识库助手.md | 401 | except Exception as e: | CODE |
| LOW | …s/InternLM/06-InternLM接入LangChain搭建知识库助手/run_gradio.py | 61 | except Exception as e: | CODE |
| LOW | …VL/02-Qwen2-VL-2B-Instruct Web Demo 参考代码/mm_qwen2vl.py | 167 | except Exception: | CODE |
| LOW | models/Qwen2.5/06-Qwen2.5-7B-Instruct o1-like 推理链实现.md | 118 | except Exception as e: | CODE |
| LOW | models/Gemma3/8-gemma3-4b-it 模型服务部署.md | 86 | except Exception as e: | CODE |
| LOW | models/Gemma3/01-gemma-3-4b-it FastApi 部署调用.md | 136 | except Exception as e: | CODE |
| LOW | models/Gemma3/01-gemma-3-4b-it FastApi 部署调用.md | 146 | except Exception as e: | CODE |
| LOW | models/Gemma3/01-gemma-3-4b-it FastApi 部署调用.md | 210 | except Exception as e: | CODE |
| LOW | models/Atom/02-Atom-7B-Chat Lora 微调.md | 298 | except Exception as e: | CODE |
| LOW | models/Atom/03-Atom-7B-Chat 接入langchain搭建知识库助手.md | 443 | except Exception as e: | CODE |
| 52 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | models/Qwen3/10-Qwen3-8B GRPO微调及通过swanlab可视化.md | 427 | # ======================== | COMMENT |
| MEDIUM | models/Qwen3/10-Qwen3-8B GRPO微调及通过swanlab可视化.md | 429 | # ======================== | COMMENT |
| MEDIUM | models_mlx/run_app_gradio.py | 20 | # ============================================================ | COMMENT |
| MEDIUM | models_mlx/run_app_gradio.py | 22 | # ============================================================ | COMMENT |
| MEDIUM⚡ | models_mlx/run_app_gradio.py | 131 | # ============================================================ | COMMENT |
| MEDIUM⚡ | models_mlx/run_app_gradio.py | 133 | # ============================================================ | COMMENT |
| MEDIUM | models_mlx/run_app_gradio.py | 307 | # ============================================================ | COMMENT |
| MEDIUM | models_mlx/run_app_gradio.py | 309 | # ============================================================ | COMMENT |
| MEDIUM | models_mlx/modules/framework.py | 104 | # ============================================================ | COMMENT |
| MEDIUM | models_mlx/modules/framework.py | 106 | # ============================================================ | COMMENT |
| MEDIUM⚡ | models_mlx/modules/download_model.py | 14 | # ============================================================ | COMMENT |
| MEDIUM⚡ | models_mlx/modules/download_model.py | 16 | # ============================================================ | COMMENT |
| MEDIUM⚡ | models_mlx/modules/download_model.py | 23 | # ============================================================ | COMMENT |
| MEDIUM⚡ | models_mlx/modules/download_model.py | 25 | # ============================================================ | COMMENT |
| MEDIUM | models_mlx/modules/download_model.py | 183 | # ============================================================ | COMMENT |
| MEDIUM | models_mlx/modules/download_model.py | 185 | # ============================================================ | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | utils.py | 2 | CODE | |
| LOW | utils.py | 3 | CODE | |
| LOW | …uct Lora 可视化微调案例 - LaTexOCR/compare_qwen3_vl_infer.py | 2 | CODE | |
| LOW | …uct Lora 可视化微调案例 - LaTexOCR/compare_qwen3_vl_infer.py | 3 | CODE | |
| LOW | …uct Lora 可视化微调案例 - LaTexOCR/compare_qwen3_vl_infer.py | 3 | CODE | |
| LOW | …ChatGLM/05-ChatGLM3-6B接入LangChain搭建知识库助手/run_gradio.py | 15 | CODE | |
| LOW | …s/InternLM/06-InternLM接入LangChain搭建知识库助手/run_gradio.py | 5 | CODE | |
| LOW | …2B-Instruct FastApi 参考代码/api_server_image_and_video.py | 3 | CODE | |
| LOW | …-Qwen2-VL-2B-Instruct FastApi 参考代码/api_server_image.py | 3 | CODE | |
| LOW | …-Instruct FastApi 参考代码/qwen_vl_utils/vision_process.py | 1 | CODE | |
| LOW | …/Atom/03-Atom-7B-Chat 接入langchain搭建知识库助手/run_gradio.py | 5 | CODE | |
| LOW | models/Atom/02-Atom-7B-Chat-Lora/train.py | 15 | CODE | |
| LOW | models/Atom/02-Atom-7B-Chat-Lora/train.py | 18 | CODE | |
| LOW | models/Gemma4/api.py | 2 | CODE | |
| LOW | …dels/MiniCPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/trainer.py | 5 | CODE | |
| LOW | …CPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/minicpm_datasets.py | 2 | CODE | |
| LOW | …CPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/minicpm_datasets.py | 5 | CODE | |
| LOW | …CPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/minicpm_datasets.py | 8 | CODE | |
| LOW | …CPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/minicpm_datasets.py | 8 | CODE | |
| LOW | …CPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/minicpm_datasets.py | 9 | CODE | |
| LOW | …CPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/minicpm_datasets.py | 9 | CODE | |
| LOW | …CPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/minicpm_datasets.py | 16 | CODE | |
| LOW | …CPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/minicpm_datasets.py | 16 | CODE | |
| LOW | models/MiniCPM/train.py | 6 | CODE | |
| LOW | models/Kimi-VL/01-Kimi-VL-对话助手/app/app.py | 9 | CODE | |
| LOW | …/Qwen/07-Qwen-7B-Chat 接入langchain搭建知识库助手/run_gradio.py | 5 | CODE | |
| LOW | models/XVERSE/code/LLM.py | 4 | CODE | |
| LOW | models/XVERSE/code/LLM.py | 4 | CODE | |
| LOW | models/XVERSE/code/model_download.py | 1 | CODE | |
| LOW | models/XVERSE/code/model_download.py | 2 | CODE | |
| LOW | models/XVERSE/code/model_download.py | 2 | CODE | |
| LOW | models/XVERSE/code/model_download.py | 3 | CODE | |
| LOW | models/BlueLM/04-BlueLM-7B-Chat Lora 微调.py | 3 | CODE | |
| LOW | examples/Chat-嬛嬛/train.py | 4 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | utils.py | 91 | CODE | |
| LOW | …/ChatGLM/05-ChatGLM3-6B接入LangChain搭建知识库助手/create_db.py | 23 | CODE | |
| LOW | …els/InternLM/06-InternLM接入LangChain搭建知识库助手/creat_db.py | 11 | CODE | |
| LOW | …-Instruct FastApi 参考代码/qwen_vl_utils/vision_process.py | 82 | CODE | |
| LOW | …-Instruct FastApi 参考代码/qwen_vl_utils/vision_process.py | 303 | CODE | |
| LOW | …VL/02-Qwen2-VL-2B-Instruct Web Demo 参考代码/mm_qwen2vl.py | 54 | CODE | |
| LOW | …VL/02-Qwen2-VL-2B-Instruct Web Demo 参考代码/mm_qwen2vl.py | 229 | CODE | |
| LOW | …ls/Atom/03-Atom-7B-Chat 接入langchain搭建知识库助手/creat_db.py | 13 | CODE | |
| LOW | models/Gemma4/verify_gemma4_tutorials.py | 66 | CODE | |
| LOW | models/Gemma4/verify_gemma4_tutorials.py | 134 | CODE | |
| LOW | models/Gemma4/api.py | 107 | CODE | |
| LOW | …CPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/minicpm_datasets.py | 231 | CODE | |
| LOW | …CPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/minicpm_datasets.py | 271 | CODE | |
| LOW | …CPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/minicpm_datasets.py | 310 | CODE | |
| LOW | …CPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/minicpm_datasets.py | 427 | CODE | |
| LOW | models/Kimi-VL/01-Kimi-VL-对话助手/app/app.py | 167 | CODE | |
| LOW | …ls/Qwen/07-Qwen-7B-Chat 接入langchain搭建知识库助手/creat_db.py | 11 | CODE | |
| LOW | models_mlx/run_app_gradio.py | 329 | CODE | |
| LOW | models_mlx/modules/download_model.py | 120 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | models/Qwen3/10-Qwen3-8B GRPO微调及通过swanlab可视化.md | 717 | # Step 1: 构造初始输入并生成输出(不加载 LoRA) | COMMENT |
| LOW | models/Qwen3/10-Qwen3-8B GRPO微调及通过swanlab可视化.md | 738 | # Step 2: 保存 GRPO 微调得到的 LoRA 权重 | COMMENT |
| LOW | models/Qwen3/10-Qwen3-8B GRPO微调及通过swanlab可视化.md | 741 | # Step 3: 检查保存的 safetensors 权重不为全零 | COMMENT |
| LOW | models/Qwen3/10-Qwen3-8B GRPO微调及通过swanlab可视化.md | 752 | # Step 4: 构造消息格式输入并应用 tokenizer 的 chat_template | COMMENT |
| LOW | models/Qwen3/10-Qwen3-8B GRPO微调及通过swanlab可视化.md | 765 | # Step 5: 加载微调后的 LoRA 并生成输出 | COMMENT |
| LOW | examples/Chat-嬛嬛/readme.md | 15 | ## Step 1: 环境准备 | COMMENT |
| LOW | examples/Chat-嬛嬛/readme.md | 44 | ## Step 2: 数据准备 | COMMENT |
| LOW | examples/Chat-嬛嬛/readme.md | 130 | ## Step 3: 模型训练 | COMMENT |
| LOW | examples/AMchat-高等数学/readme.md | 26 | ### Step 1: 数据准备 | COMMENT |
| LOW | examples/AMchat-高等数学/readme.md | 49 | ### Step 2: 环境准备 | COMMENT |
| LOW | examples/AMchat-高等数学/readme.md | 66 | ### Step 3: 模型微调 | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | models/Gemma3/6-gemma3-4B-itGRPO微调及通过swanlab可视化.md | 258 | def match_format_approximately(completions, **kwargs): | CODE |
| LOW | models/Gemma4/verify_gemma4_tutorials.py | 66 | def test_fastapi_smoke_with_mock() -> bool: | CODE |
| LOW | models/Gemma4/6-gemma4-E4B-itGRPO微调及通过swanlab可视化.md | 260 | def match_format_approximately(completions, **kwargs): | CODE |
| LOW | models/phi4/06-Phi-4-GRPO及swanlab可视化.md | 229 | def strict_format_reward_func(completions, **kwargs) -> list[float]: | STRING |
| LOW | models/MiniCPM-o/04-MiniCPM-0-2.6 Lora微调.md | 259 | def make_supervised_data_module( | CODE |
| LOW | models/MiniCPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/train.py | 17 | def make_supervised_data_module( | CODE |
| LOW | …CPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/minicpm_datasets.py | 199 | def conversation_to_ids_minicpm(conversation, tokenizer): | CODE |
| LOW | models/Qwen3/10-Qwen3-8B GRPO微调及通过swanlab可视化.md | 474 | def match_format_approximately(completions, **kwargs): | CODE |
| LOW | …n-A13B-Instruct/03-Hunyuan-A13B-Instruct-SGLang部署调用.md | 157 | def chatcompletionwiththinkbyargs(messages, enable_thinking=True): | CODE |
| LOW | …l-Qwen/05-DeepSeek-R1-0528-Qwen3-8B-GRPO及swanlab可视化.md | 198 | def match_format_approximately(completions, **kwargs): | CODE |
| LOW | …l-Qwen/05-DeepSeek-R1-0528-Qwen3-8B-GRPO及swanlab可视化.md | 287 | def format_and_language_reward_func(completions, **kwargs): | CODE |
| LOW | examples/数字生命/readme.md | 23 | def convert_to_sharegpt_format(original_data,new_system_value=None): | CODE |
| LOW | examples/Tianji-天机/readme.md | 225 | def extract_and_merge_conversations(folder_path, output_file): | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | models/GLM-4/04-GLM-4-9B-Chat vLLM 部署调用.md | 101 | # tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False) # 加载分词器后传入vLLM 模型,但不是必要的。 | COMMENT |
| LOW | models/Qwen2.5/03-Qwen2.5-7B-Instruct vLLM 部署调用.md | 101 | COMMENT | |
| LOW | models/MiniCPM-o/03-MiniCPM-o-2.6 多模态语音能力.md | 141 | COMMENT | |
| LOW | models/Qwen2/04-Qwen2-7B-Instruct vLLM 部署调用.md | 121 | # tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False) | COMMENT |
| LOW | models/Qwen/08-Qwen-7B-Chat Lora 低精度微调.py | 61 | tokenized_id = ds.map(process_func, remove_columns=ds.column_names) | COMMENT |
| LOW | models/Qwen1.5/07-Qwen1.5-7B-Chat vLLM 推理部署调用.md | 101 | if __name__ == "__main__": | COMMENT |
| LOW | models/XVERSE/code/data_format.py | 21 | with open('ruozhiba.json', 'w', encoding='utf-8') as f: | COMMENT |
| LOW | models/InternLM3/04-InternLM3-8B-Instruct LoRA.md | 201 | > 注意:此处要记得修改为自己的模型路径哦~ | COMMENT |
| LOW | models/InternLM3/04-InternLM3-8B-Instruct LoRA.md | 221 | # ) | COMMENT |
| LOW | …still-Qwen/04-DeepSeek-R1-Distill-Qwen-7B vLLM 部署调用.md | 101 | # tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False) | COMMENT |
| LOW | examples/Tianji-天机/readme.md | 41 | from zhipuai import ZhipuAI | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | utils.py | 21 | # Read files | COMMENT |
| LOW | utils.py | 61 | # Check if the task contains "Lora" or "微调" (case-insensitive) | COMMENT |
| LOW | utils.py | 84 | # Print results | COMMENT |
| LOW | models/GLM-4/benchmark_throughput.py | 155 | # Check if we can add more requests to the batch. | COMMENT |
| LOW | models/Qwen2.5/benchmark_throughput.py | 257 | # Check if we can add more requests to the batch. | COMMENT |
| LOW | models/Qwen2/benchmark_throughput.py | 154 | # Check if we can add more requests to the batch. | COMMENT |
| LOW | models/Qwen1.5/benchmark_throughput.py | 154 | # Check if we can add more requests to the batch. | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | …-Instruct FastApi 参考代码/qwen_vl_utils/vision_process.py | 132 | calculate the number of frames for video used for model inputs. Args: ele (dict): a dict contains the confi | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | …dels/GLM-4.7-Flash/03-GLM-4.7-Flash-Lora微调及Docker镜像.md | 137 | swanlab.login(api_key='your-apikey', save=True) # 记得替换为自己账号的apikey | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …-Instruct FastApi 参考代码/qwen_vl_utils/vision_process.py | 22 | logger = logging.getLogger(__name__) | CODE |
| LOW | models/Gemma4/api.py | 17 | logger = logging.getLogger(__name__) | CODE |
| LOW | …CPM-o/04-MiniCPM-0-2.6 Lora微调 参考代码/minicpm_datasets.py | 19 | logger = logging.getLogger(__name__) | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | utils.py | 149 | # Usage example | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | models/XVERSE/code/LLM.py | 30 | CODE |