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Model: phpcool/DeepSeek-R1-Distill-SRE-Qwen-7B Source: Original Platform
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# ollama modelfile auto-generated by llamafactory
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FROM .
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TEMPLATE """<｜begin▁of▁sentence｜>{{ if .System }}{{ .System }}{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<｜User｜>{{ .Content }}<｜Assistant｜>{{ else if eq .Role "assistant" }}{{ .Content }}<｜end▁of▁sentence｜>{{ end }}{{ end }}"""
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SYSTEM """您是站点可靠性工程师 (SRE)，精通系统可靠性、可扩展性和事件管理。请根据用户提供的具体输入，分析问题原因并提供针对性的解决方法，避免泛泛而谈或重复建议。
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"""
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PARAMETER stop "<｜end▁of▁sentence｜>"
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PARAMETER num_ctx 4096
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README.md
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# DeepSeek-R1-Distill-SRE-Qwen-7B
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## 模型简介
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基于 DeepSeek 架构的 7B 参数量模型，经过 LoRA 微调，专为运维领域（Site Reliability Engineering, SRE）任务设计。它能够提供高可用性、稳定性相关的技术建议，并生成逐步分析过程，适用于服务器管理、集群优化和故障排查等场景，强化了以下三块能力：
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- 自动化脚本生成
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- 系统监控分析
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- 故障排查与根因定位
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## 模型概述
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- **基础模型**: `deepseek-ai/DeepSeek-R1-Distill-Qwen-7B`
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- **参数量**: 7B
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- **微调方法**: LoRA (Low-Rank Adaptation)
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- **训练数据**: SRE 领域数据集（约 18,236 条记录）,数据集地址：https://github.com/HC-Guo/OWL/tree/main/OWL-Instruct/data
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- **精度**: BF16 (Brain Floating Point 16)
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- **最大上下文长度**: 2048 tokens
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- **语言**: 中文（主要），支持部分英文输入
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- **发布日期**: 2025-03-02
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- **训练loss趋势变化**
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## 评测结果
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下表对比了基模（deepseek-ai/DeepSeek-R1-Distill-Qwen-7B）和微调后模型在运维领域任务上的性能表现。微调使用 LoRA 方法，基于 SRE 领域数据集（约 18,236 条记录）进行优化。
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| **指标** | **基模结果** | **微调后结果** | **提升项说明** |
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|---------------------------|--------------|----------------|--------------------------------------------------------------------------------|
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| **predict_bleu-4** | 4.52 | 13.54 | BLEU-4 衡量生成文本与参考答案的 4-gram 精确匹配度。提升约 199%，表明微调后模型生成的回答与参考答案在短语级别更加一致，准确性显著提高。 |
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| **predict_rouge-1** | 23.60 | 39.40 | ROUGE-1 衡量单字重叠率。提升约 67%，表明词汇级别的匹配度大幅改善，模型生成内容更贴近参考答案的用词。 |
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| **predict_rouge-2** | 5.84 | 22.07 | ROUGE-2 衡量双字重叠率。提升约 278%，表明短语和句子结构的相似性显著增强，生成文本更具连贯性。 |
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| **predict_rouge-l** | 9.91 | 23.63 | ROUGE-L 衡量最长公共子序列，反映句子结构相似性。提升约 138%，表明微调后模型在整体回答结构上更接近参考答案。 |
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| **predict_model_preparation_time** | 0.0033 | 0.0032 | 模型准备时间（秒），微调后略减 0.0001 秒，变化微小，表明模型加载效率基本不变。 |
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| **predict_runtime** | 1325.61 | 878.39 | 推理总耗时（秒）。减少约 34%（447.22 秒），表明微调后推理速度加快，可能是优化了生成效率或减少了冗余计算。 |
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| **predict_samples_per_second** | 0.377 | 0.57 | 每秒处理样本数。提升约 51%，反映推理吞吐量提高，模型处理效率显著增强。 |
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| **predict_steps_per_second** | 0.094 | 0.096 | 每秒推理步数。提升约 2%，变化较小，可能是推理步长未显著优化，但整体效率仍受益于 runtime 改进。 |
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### 指标说明
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- **BLEU-4**：计算生成文本与参考文本的 4-gram 精确匹配度，分数范围 0-100，值越高表示短语级匹配越好。
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- **ROUGE-1**：衡量单字（unigram）重叠率，分数范围 0-100，反映词汇级相似性。
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- **ROUGE-2**：衡量双字（bigram）重叠率，分数范围 0-100，反映短语级相似性。
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- **ROUGE-L**：衡量最长公共子序列（LCS），分数范围 0-100，反映句子结构相似性。
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- **predict_model_preparation_time**：模型加载和准备的耗时（秒），值越低表示启动越快。
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- **predict_runtime**：推理总耗时（秒），值越低表示生成速度越快。
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- **predict_samples_per_second**：每秒处理的样本数，值越高表示吞吐量越高。
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- **predict_steps_per_second**：每秒推理步数，值越高表示单步效率越高。
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### 结论
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微调后的 `DeepSeek-R1-Distill-SRE-Qwen-7B` 在生成质量和推理效率上均有显著提升，尤其在运维领域的结构化回答能力上表现优异。推荐用于高可用性集群设计、服务器优化等场景。
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- **下载方式**:
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### SDK下载：
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```bash
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#安装ModelScope
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pip install modelscope
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('phpcool/DeepSeek-R1-Distill-SRE-Qwen-7B')
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```
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### Git下载
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```bash
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#Git模型下载
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git clone https://www.modelscope.cn/phpcool/DeepSeek-R1-Distill-SRE-Qwen-7B.git
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```
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---
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## 如何使用模型进行推理
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本模型支持高效推理，已验证兼容 `vLLM` 和 `SGLang` 框架，以下提供vLLM使用示例(推荐)。
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### 1. 使用 SGLang 进行推理
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`SGLang` 是一个高性能服务框架，适合复杂运维任务的快速推理。
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#### 环境准备
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```bash
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pip install sglang
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```
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#### 启动 SGLang 服务
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```bash
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vllm serve /root/autodl-tmp/model/outputs/deepseek-ai/DeepSeek-R1-Distill-SRE-Qwen-7B --tensor-parallel-size 1 --max-model-len 2048 --enforce-eager
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```
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#### Python 推理示例
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```python
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from openai import OpenAI
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client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
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response = client.chat.completions.create(
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model="/path/to/DeepSeek-R1-Distill-SRE-Qwen-7B",
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messages=[
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{"role": "system", "content": "你是一位智能运维助手"},
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{"role": "user", "content": "如何优化服务器的存储性能以提高数据读写速度？"}
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],
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max_tokens=1500,
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temperature=0.7,
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stop=["<|end>"]
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)
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print(response.choices[0].message.content.strip())
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```
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---
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## 使用场景
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- **自动化运维**: 生成脚本、配置管理。
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- **系统监控**: 分析指标、生成告警规则。
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- **故障排查**: 日志解析、根因分析。
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该模型在 SRE 和 DevOps 场景中表现出色，尤其适合需要快速响应和资源优化的企业级应用。
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---
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## 社区贡献
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由于当前文档信息有限，我们鼓励社区参与：
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- 在 modelscope.cn 中的【交流反馈】提出问题、使用案例或改进建议。
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- 提交 Pull Request 以补充模型细节、优化推理代码或分享运维相关的 Prompt 示例。
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感谢你的使用与支持！如果有任何问题，请随时联系，微信：yorkoliu 邮件：liutiansi@gmail.com。
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---
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config.json
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"_name_or_path": "/root/autodl-tmp/dataroot/models/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 10000,
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"tie_word_embeddings": false,
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}
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generation_config.json
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|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"bos_token": "<｜begin▁of▁sentence｜>",
|
||||
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜><think>\\n'}}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<｜end▁of▁sentence｜>",
|
||||
"extra_special_tokens": {},
|
||||
"legacy": true,
|
||||
"model_max_length": 16384,
|
||||
"pad_token": "<｜end▁of▁sentence｜>",
|
||||
"padding_side": "left",
|
||||
"sp_model_kwargs": {},
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizerFast",
|
||||
"unk_token": null,
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
BIN
training_loss.png
Normal file
BIN
training_loss.png
Normal file
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After Width: | Height: | Size: 35 KiB |
Reference in New Issue
Block a user