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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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# 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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{
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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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"hidden_act": "silu",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"max_position_embeddings": 131072,
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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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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.49.0",
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"use_cache": true,
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"use_mrope": false,
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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configuration.json
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{"task":"other"}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 151646,
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"do_sample": true,
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"eos_token_id": 151643,
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"temperature": 0.6,
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"top_p": 0.95,
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"transformers_version": "4.49.0"
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}
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model-00001-of-00004.safetensors
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model-00001-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:52684e31e9228546ff030f036da966e5691b8c9137d8fd0af8dd78f2144d1ad2
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size 4877660776
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version https://git-lfs.github.com/spec/v1
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oid sha256:702c0434c6f2010f3f917f46b53030c63cc8ca4e5a2ad11f11682e14aa2ee81c
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size 4932751008
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version https://git-lfs.github.com/spec/v1
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oid sha256:e98a76e754d2f8fea460c4684bdd49a9ae84bc1429431c0e592dc6586396f0df
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size 4330865200
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version https://git-lfs.github.com/spec/v1
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oid sha256:7946740ab848b4a02904f05bff81211bf9600fc20bfad39bbcc8d1703a40ce1c
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size 1089994880
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model.safetensors.index.json
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{
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"metadata": {
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"total_size": 15231233024
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},
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"weight_map": {
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"lm_head.weight": "model-00004-of-00004.safetensors",
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23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
|||||||
|
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|
||||||
|
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|
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|
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|
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|
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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|
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size 11422778
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197
tokenizer_config.json
Normal file
197
tokenizer_config.json
Normal file
@@ -0,0 +1,197 @@
|
|||||||
|
{
|
||||||
|
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|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151644": {
|
||||||
|
"content": "<|User|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151645": {
|
||||||
|
"content": "<|Assistant|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151646": {
|
||||||
|
"content": "<|begin▁of▁sentence|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151647": {
|
||||||
|
"content": "<|EOT|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151648": {
|
||||||
|
"content": "<think>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151649": {
|
||||||
|
"content": "</think>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151650": {
|
||||||
|
"content": "<|quad_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151651": {
|
||||||
|
"content": "<|quad_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151652": {
|
||||||
|
"content": "<|vision_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151653": {
|
||||||
|
"content": "<|vision_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151654": {
|
||||||
|
"content": "<|vision_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151655": {
|
||||||
|
"content": "<|image_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151656": {
|
||||||
|
"content": "<|video_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151657": {
|
||||||
|
"content": "<tool_call>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151658": {
|
||||||
|
"content": "</tool_call>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151659": {
|
||||||
|
"content": "<|fim_prefix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151660": {
|
||||||
|
"content": "<|fim_middle|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151661": {
|
||||||
|
"content": "<|fim_suffix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151662": {
|
||||||
|
"content": "<|fim_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"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
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"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
Binary file not shown.
|
After Width: | Height: | Size: 35 KiB |
Reference in New Issue
Block a user