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gaussdb-sql-expert-7b

Text Generation
Transformers
Safetensors
Chinese
English
qwen2
sql
text2sql
database
gaussdb
lora
fine-tuned
conversational
Eval Results (legacy)
text-generation-inference
Model card Files
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  1. .gitattributes +1 -0
  2. README.md +234 -0
  3. chat_template.jinja +54 -0
  4. config.json +61 -0
  5. generation_config.json +14 -0
  6. model-00001-of-00004.safetensors +3 -0
  7. model-00002-of-00004.safetensors +3 -0
  8. model-00003-of-00004.safetensors +3 -0
  9. model-00004-of-00004.safetensors +3 -0
  10. model.safetensors.index.json +347 -0
  11. tokenizer.json +3 -0
  12. tokenizer_config.json +29 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,234 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ language:
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+ - zh
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+ - en
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+ base_model: Qwen/Qwen2.5-Coder-7B-Instruct
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+ tags:
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+ - sql
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+ - text2sql
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+ - database
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+ - gaussdb
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+ - lora
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+ - fine-tuned
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+ pipeline_tag: text-generation
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+ library_name: transformers
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+ datasets:
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+ - custom
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+ model-index:
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+ - name: GaussDB-SQL-Expert-7B
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+ results:
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+ - task:
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+ type: text-generation
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+ name: Database SQL Expert
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+ metrics:
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+ - name: Text2SQL Accuracy
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+ type: accuracy
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+ value: 100
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+ - name: SQL Migration Accuracy
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+ type: accuracy
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+ value: 100
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+ - name: Error Diagnosis Accuracy
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+ type: accuracy
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+ value: 100
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+ - name: SQL Tuning Accuracy
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+ type: accuracy
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+ value: 90
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+ - name: Boundary Safety Accuracy
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+ type: accuracy
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+ value: 80
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+ - name: Overall Accuracy
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+ type: accuracy
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+ value: 94
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+ ---
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+
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+ # GaussDB SQL Expert 7B
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+
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+ 基于 Qwen2.5-Coder-7B-Instruct 微调的企业级数据库智能助手，专精 SQL 生成、调优、迁移、诊断等数据库领域任务。
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+
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+ ## 模型概述
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+
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+ | 项目 | 详情 |
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+ |------|------|
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+ | 基座模型 | [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) |
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+ | 参数量 | 7.6B (Dense) |
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+ | 微调方法 | LoRA (rank=64, alpha=128, target=all linear layers) |
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+ | 可训参数 | 161M (2.08%) |
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+ | 训练数据 | 29,863 条 ShareGPT 多轮对话 + 1,571 条验证 |
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+ | 训练硬件 | 1×NVIDIA H100 80GB |
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+ | 训练耗时 | 3.5 小时 |
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+ | 训练框架 | [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) v0.9.4 |
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+ | 精度 | BF16 |
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+
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+ ## 核心能力
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+
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+ - **Text2SQL**: 自然语言转 SQL，支持窗口函数、递归 CTE、MERGE、子查询等复杂语法
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+ - **SQL 调优**: 索引失效分析、执行计划解读、参数配置优化建议
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+ - **SQL 迁移**: Oracle / MySQL / SQL Server → GaussDB 语法自动转换 (50+ 差异点)
68
+ - **错误诊断**: 死锁、WAL 膨胀、连接耗尽、OOM 等 20+ 常见故障场景
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+ - **SQL 解释**: 复杂查询的逻辑拆解与可读性分析
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+ - **边界安全**: 危险操作拦截、信息不足追问、超范围拒绝
71
+
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+ **支持 9 种主流数据库**: GaussDB, Oracle, MySQL, PostgreSQL, SQL Server, PolarDB, 达梦(DM), 金仓(KingBase), Sybase
73
+
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+ ## 评测结果
75
+
76
+ 使用 100 道自动化评测题（每类 20 道），关键词匹配评分：
77
+
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+ | 维度 | 得分 | 说明 |
79
+ |------|------|------|
80
+ | Text2SQL | 20/20 (100%) | 窗口函数、CTE、MERGE、分页等全部正确 |
81
+ | SQL 调优 | 18/20 (90%) | 索引失效、隐式转换、参数调优等 |
82
+ | SQL 迁移 | 20/20 (100%) | Oracle/MySQL/SQL Server → GaussDB 转换 |
83
+ | 错误诊断 | 20/20 (100%) | 死锁、WAL、OOM、连接耗尽等 |
84
+ | 边界安全 | 16/20 (80%) | 危险操作告警、超范围拒绝 |
85
+ | **综合** | **94/100 (94%)** | |
86
+
87
+ ## 快速开始
88
+
89
+ ### 安装依赖
90
+
91
+ ```bash
92
+ pip install torch transformers
93
+ ```
94
+
95
+ ### Python 推理
96
+
97
+ ```python
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+ import torch
99
+ from transformers import AutoModelForCausalLM, AutoTokenizer
100
+
101
+ model_path = "your-username/gaussdb-sql-expert-7b" # 替换为实际 repo 名
102
+ device = "cuda" # 或 "mps" (Mac) / "cpu"
103
+
104
+ tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
105
+ model = AutoModelForCausalLM.from_pretrained(
106
+ model_path,
107
+ torch_dtype=torch.float16,
108
+ device_map=device,
109
+ trust_remote_code=True,
110
+ )
111
+ model.eval()
112
+
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+ messages = [
114
+ {"role": "system", "content": "你是一个专业的数据库专家助手，精通 Oracle, MySQL, PostgreSQL, GaussDB, PolarDB, 达梦(DM), 金仓(KingBase), Sybase, SQL Server。"},
115
+ {"role": "user", "content": "数据库类型: GaussDB 505 集中式\n\n查询每个部门工资最高的前3名员工，显示部门名、员工名、工资和排名。"},
116
+ ]
117
+
118
+ text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
119
+ inputs = tokenizer(text, return_tensors="pt").to(device)
120
+
121
+ with torch.no_grad():
122
+ outputs = model.generate(
123
+ **inputs,
124
+ max_new_tokens=1024,
125
+ temperature=0.7,
126
+ do_sample=True,
127
+ top_p=0.8,
128
+ repetition_penalty=1.05,
129
+ )
130
+
131
+ response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
132
+ print(response)
133
+ ```
134
+
135
+ ### 示例输出
136
+
137
+ **输入**: 查询每个部门工资最高的前3名员工
138
+
139
+ **输出**:
140
+ ```sql
141
+ SELECT dept_name, emp_name, salary, rk
142
+ FROM (
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+ SELECT d.dept_name, e.emp_name, e.salary,
144
+ DENSE_RANK() OVER(PARTITION BY e.dept_id ORDER BY e.salary DESC) AS rk
145
+ FROM employees e
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+ JOIN departments d ON e.dept_id = d.dept_id
147
+ ) t
148
+ WHERE rk <= 3;
149
+ ```
150
+
151
+ **输入**: Oracle → GaussDB 迁移: `SELECT NVL(name, '未知') FROM users WHERE ROWNUM <= 10`
152
+
153
+ **输出**:
154
+ ```sql
155
+ SELECT COALESCE(name, '未知') FROM users LIMIT 10;
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+ -- NVL → COALESCE, ROWNUM → LIMIT
157
+ ```
158
+
159
+ ## 训练详情
160
+
161
+ ### 训练超参数
162
+
163
+ ```yaml
164
+ # LoRA 配置
165
+ lora_rank: 64
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+ lora_alpha: 128
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+ lora_dropout: 0.05
168
+ lora_target: all # q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
169
+
170
+ # 训练参数
171
+ learning_rate: 2.0e-5
172
+ lr_scheduler_type: cosine
173
+ warmup_ratio: 0.1
174
+ num_train_epochs: 3
175
+ per_device_train_batch_size: 8
176
+ gradient_accumulation_steps: 4 # 等效 batch_size = 32
177
+ cutoff_len: 2048
178
+ optim: adamw_torch
179
+ bf16: true
180
+ gradient_checkpointing: true
181
+ ```
182
+
183
+ ### 训练 Loss 曲线
184
+
185
+ ```
186
+ 训练过程：2,799 步，3 小时 29 分钟
187
+
188
+ Step Epoch Train Loss Eval Loss
189
+ 200 0.21 1.217 1.216
190
+ 600 0.64 1.038 1.104
191
+ 1000 1.07 1.035 1.076
192
+ 1400 1.50 1.062 1.058
193
+ 1800 1.93 1.062 1.045
194
+ 2200 2.36 0.966 1.044
195
+ 2600 2.79 0.959 1.042 ← 最优检查点
196
+ ```
197
+
198
+ 最终 train_loss=1.039, eval_loss=1.042，两者接近，无过拟合。
199
+
200
+ ### 训练数据分布
201
+
202
+ | 场景 | 占比 | 说明 |
203
+ |------|------|------|
204
+ | Text2SQL | ~30% | 自然语言 → SQL 生成 |
205
+ | SQL 调优 | ~20% | 慢查询分析、索引优化 |
206
+ | SQL 迁移 | ~15% | 跨数据库语法转换 |
207
+ | 错误诊断 | ~15% | 生产故障排查 |
208
+ | 运维知识 | ~10% | 参数调优、备份恢复 |
209
+ | 边界安全 | ~10% | 危险操作告警、超范围拒绝 |
210
+
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+ ## 局限性
212
+
213
+ - 边界安全能力还有提升空间：对 DELETE 全表、DROP DATABASE 等操作可能直接执行而不告警
214
+ - 对 GaussDB 505 特有的高级功能（如列存表、分布式特性）覆盖有限
215
+ - 仅支持文本输入，不支持图片（如执行计划截图）
216
+ - 建议在生产环境中增加推理侧安全规则兜底
217
+
218
+ ## 引用
219
+
220
+ 如果本模型对你有帮助，欢迎引用：
221
+
222
+ ```bibtex
223
+ @misc{gaussdb-sql-expert-7b,
224
+ title={GaussDB SQL Expert 7B},
225
+ author={lilanfeng},
226
+ year={2026},
227
+ publisher={HuggingFace},
228
+ url={https://huggingface.co/your-username/gaussdb-sql-expert-7b}
229
+ }
230
+ ```
231
+
232
+ ## 许可证
233
+
234
+ 本模型基于 [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) 微调，遵循 Apache 2.0 许可证。
chat_template.jinja ADDED
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+ {%- if tools %}
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+ {{- '<|im_start|>system\n' }}
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+ {%- if messages[0]['role'] == 'system' %}
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+ {{- messages[0]['content'] }}
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+ {%- else %}
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+ {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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+ {%- endif %}
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+ {{- "\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>" }}
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+ {%- for tool in tools %}
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+ {{- "\n" }}
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+ {{- tool | tojson }}
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+ {%- endfor %}
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+ {{- "\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" }}
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+ {%- else %}
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+ {%- if messages[0]['role'] == 'system' %}
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+ {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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+ {%- else %}
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+ {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- for message in messages %}
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+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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+ {%- elif message.role == "assistant" %}
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+ {{- '<|im_start|>' + message.role }}
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+ {%- if message.content %}
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+ {{- '\n' + message.content }}
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+ {%- endif %}
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+ {%- for tool_call in message.tool_calls %}
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+ {%- if tool_call.function is defined %}
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+ {%- set tool_call = tool_call.function %}
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+ {%- endif %}
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+ {{- '\n<tool_call>\n{"name": "' }}
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+ {{- tool_call.name }}
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+ {{- '", "arguments": ' }}
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+ {{- tool_call.arguments | tojson }}
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+ {{- '}\n</tool_call>' }}
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+ {%- endfor %}
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+ {{- '<|im_end|>\n' }}
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+ {%- elif message.role == "tool" %}
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+ {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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+ {{- '<|im_start|>user' }}
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+ {%- endif %}
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+ {{- '\n<tool_response>\n' }}
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+ {{- message.content }}
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+ {{- '\n</tool_response>' }}
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+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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+ {{- '<|im_end|>\n' }}
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+ {%- endif %}
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+ {%- endif %}
51
+ {%- endfor %}
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+ {%- if add_generation_prompt %}
53
+ {{- '<|im_start|>assistant\n' }}
54
+ {%- endif %}
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