From c94f6d5e23563f228951c6e961b65b20ffcc985e Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sat, 13 Jun 2026 10:34:16 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: lanfers/gaussdb-sql-expert-7b Source: Original Platform --- .gitattributes | 36 ++++ README.md | 272 ++++++++++++++++++++++++ README_zh.md | 237 +++++++++++++++++++++ chat_template.jinja | 54 +++++ config.json | 61 ++++++ generation_config.json | 14 ++ model-00001-of-00004.safetensors | 3 + model-00002-of-00004.safetensors | 3 + model-00003-of-00004.safetensors | 3 + model-00004-of-00004.safetensors | 3 + model.safetensors.index.json | 347 +++++++++++++++++++++++++++++++ tokenizer.json | 3 + tokenizer_config.json | 29 +++ 13 files changed, 1065 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 README_zh.md create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model-00001-of-00004.safetensors create mode 100644 model-00002-of-00004.safetensors create mode 100644 model-00003-of-00004.safetensors create mode 100644 model-00004-of-00004.safetensors create mode 100644 model.safetensors.index.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..6d1c727 --- /dev/null +++ b/README.md @@ -0,0 +1,272 @@ +--- +license: apache-2.0 +language: + - zh + - en +base_model: Qwen/Qwen2.5-Coder-7B-Instruct +tags: + - sql + - text2sql + - database + - gaussdb + - lora + - fine-tuned +pipeline_tag: text-generation +library_name: transformers +datasets: + - custom +model-index: + - name: GaussDB-SQL-Expert-7B + results: + - task: + type: text-generation + name: Database SQL Expert + metrics: + - name: Text2SQL Accuracy + type: accuracy + value: 100 + - name: SQL Migration Accuracy + type: accuracy + value: 100 + - name: Error Diagnosis Accuracy + type: accuracy + value: 100 + - name: SQL Tuning Accuracy + type: accuracy + value: 90 + - name: Boundary Safety Accuracy + type: accuracy + value: 80 + - name: Overall Accuracy + type: accuracy + value: 94 +--- + +# GaussDB SQL Expert 7B + +**[中文版 README](README_zh.md)** + +A domain-specific database assistant fine-tuned on Qwen2.5-Coder-7B-Instruct, specialized in SQL generation, optimization, cross-database migration, error diagnosis, and more. + +## Model Overview + +| Item | Details | +|------|---------| +| Base Model | [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) | +| Parameters | 7.6B (Dense) | +| Fine-tuning | LoRA (rank=64, alpha=128, target=all linear layers) | +| Trainable Params | 161M (2.08% of total) | +| Training Data | 29,863 ShareGPT conversations + 1,571 validation | +| Hardware | 1x NVIDIA H100 80GB | +| Training Time | 3.5 hours | +| Framework | [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) v0.9.4 | +| Precision | BF16 | + +## Core Capabilities + +- **Text2SQL**: Natural language to SQL with support for window functions, recursive CTEs, MERGE, subqueries, and more +- **SQL Tuning**: Index invalidation analysis, execution plan interpretation, parameter optimization advice +- **SQL Migration**: Oracle / MySQL / SQL Server → GaussDB syntax conversion (50+ difference points) +- **Error Diagnosis**: Deadlock, WAL bloat, connection exhaustion, OOM, and 20+ common production issues +- **SQL Explanation**: Logic breakdown and readability analysis of complex queries +- **Boundary Safety**: Dangerous operation interception, clarification requests, out-of-scope rejection + +**Supports 9 major databases**: GaussDB, Oracle, MySQL, PostgreSQL, SQL Server, PolarDB, DM (Dameng), KingBase, Sybase + +## Benchmark Results + +Evaluated on 100 automated test cases (20 per category) using keyword matching: + +| Category | Score | Notes | +|----------|-------|-------| +| Text2SQL | 20/20 (100%) | Window functions, CTE, MERGE, pagination all correct | +| SQL Tuning | 18/20 (90%) | Index invalidation, implicit conversion, parameter tuning | +| SQL Migration | 20/20 (100%) | Oracle/MySQL/SQL Server → GaussDB conversion | +| Error Diagnosis | 20/20 (100%) | Deadlock, WAL, OOM, connection exhaustion | +| Boundary Safety | 16/20 (80%) | Dangerous operation alerts, out-of-scope rejection | +| **Overall** | **94/100 (94%)** | | + +## Quick Start + +### Requirements + +- Python >= 3.9 +- PyTorch >= 2.0 +- GPU with >= 16GB VRAM (recommended) or CPU (slower) +- ~15GB disk space for model weights + +### Installation + +```bash +# 1. Install dependencies +pip install torch transformers accelerate + +# 2. (Optional) Install Flash Attention 2 for faster inference on NVIDIA GPUs +pip install flash-attn --no-build-isolation +``` + +### Download Model + +The model will be downloaded automatically on first use via `from_pretrained()`. You can also download it manually: + +```bash +# Option A: Using huggingface-cli +pip install huggingface_hub +huggingface-cli download lanfers/gaussdb-sql-expert-7b --local-dir ./gaussdb-sql-expert-7b + +# Option B: Using git-lfs +git lfs install +git clone https://huggingface.co/lanfers/gaussdb-sql-expert-7b + +# Option C: Using Python +python -c " +from huggingface_hub import snapshot_download +snapshot_download('lanfers/gaussdb-sql-expert-7b', local_dir='./gaussdb-sql-expert-7b') +" +``` + +> **China Mainland Users**: If download is slow, use a mirror: +> ```bash +> HF_ENDPOINT=https://hf-mirror.com huggingface-cli download lanfers/gaussdb-sql-expert-7b --local-dir ./gaussdb-sql-expert-7b +> ``` + +### Inference + +```python +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_path = "lanfers/gaussdb-sql-expert-7b" +device = "cuda" # or "mps" (Mac) / "cpu" + +tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) +model = AutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=torch.float16, + device_map=device, + trust_remote_code=True, +) +model.eval() + +messages = [ + {"role": "system", "content": "You are a professional database expert assistant, proficient in Oracle, MySQL, PostgreSQL, GaussDB, PolarDB, DM, KingBase, Sybase, SQL Server."}, + {"role": "user", "content": "Database: GaussDB 505\n\nFind the top 3 highest-paid employees in each department, showing department name, employee name, salary, and rank."}, +] + +text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) +inputs = tokenizer(text, return_tensors="pt").to(device) + +with torch.no_grad(): + outputs = model.generate( + **inputs, + max_new_tokens=1024, + temperature=0.7, + do_sample=True, + top_p=0.8, + repetition_penalty=1.05, + ) + +response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) +print(response) +``` + +### Example Outputs + +**Input**: Find the top 3 highest-paid employees in each department + +**Output**: +```sql +SELECT dept_name, emp_name, salary, rk +FROM ( + SELECT d.dept_name, e.emp_name, e.salary, + DENSE_RANK() OVER(PARTITION BY e.dept_id ORDER BY e.salary DESC) AS rk + FROM employees e + JOIN departments d ON e.dept_id = d.dept_id +) t +WHERE rk <= 3; +``` + +**Input**: Migrate Oracle to GaussDB: `SELECT NVL(name, 'unknown') FROM users WHERE ROWNUM <= 10` + +**Output**: +```sql +SELECT COALESCE(name, 'unknown') FROM users LIMIT 10; +-- NVL → COALESCE, ROWNUM → LIMIT +``` + +## Training Details + +### Hyperparameters + +```yaml +# LoRA config +lora_rank: 64 +lora_alpha: 128 +lora_dropout: 0.05 +lora_target: all # q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj + +# Training config +learning_rate: 2.0e-5 +lr_scheduler_type: cosine +warmup_ratio: 0.1 +num_train_epochs: 3 +per_device_train_batch_size: 8 +gradient_accumulation_steps: 4 # effective batch_size = 32 +cutoff_len: 2048 +optim: adamw_torch +bf16: true +gradient_checkpointing: true +``` + +### Training Loss + +``` +Total steps: 2,799 | Duration: 3h 29m + +Step Epoch Train Loss Eval Loss + 200 0.21 1.217 1.216 + 600 0.64 1.038 1.104 + 1000 1.07 1.035 1.076 + 1400 1.50 1.062 1.058 + 1800 1.93 1.062 1.045 + 2200 2.36 0.966 1.044 + 2600 2.79 0.959 1.042 ← best checkpoint +``` + +Final train_loss=1.039, eval_loss=1.042. Near-identical values indicate no overfitting. + +### Training Data Distribution + +| Category | Proportion | Description | +|----------|-----------|-------------| +| Text2SQL | ~30% | Natural language → SQL generation | +| SQL Tuning | ~20% | Slow query analysis, index optimization | +| SQL Migration | ~15% | Cross-database syntax conversion | +| Error Diagnosis | ~15% | Production incident troubleshooting | +| Operations | ~10% | Parameter tuning, backup & recovery | +| Boundary Safety | ~10% | Dangerous operation alerts, scope rejection | + +## Limitations + +- Boundary safety has room for improvement: may execute `DELETE` without `WHERE` or `DROP DATABASE` without warning +- Limited coverage of GaussDB 505 advanced features (e.g., column-store tables, distributed features) +- Text-only input; does not support images (e.g., execution plan screenshots) +- Recommended to add inference-side safety rules for production environments + +## Citation + +If this model is helpful, please cite: + +```bibtex +@misc{gaussdb-sql-expert-7b, + title={GaussDB SQL Expert 7B}, + author={lanfers}, + year={2026}, + publisher={HuggingFace}, + url={https://huggingface.co/lanfers/gaussdb-sql-expert-7b} +} +``` + +## License + +Fine-tuned from [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) under the Apache 2.0 License. diff --git a/README_zh.md b/README_zh.md new file mode 100644 index 0000000..0461cc0 --- /dev/null +++ b/README_zh.md @@ -0,0 +1,237 @@ +# GaussDB SQL Expert 7B + +**[English README](README.md)** + +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。 + +## 模型概述 + +| 项目 | 详情 | +|------|------| +| 基座模型 | [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) | +| 参数量 | 7.6B (Dense) | +| 微调方法 | LoRA (rank=64, alpha=128, target=all linear layers) | +| 可训参数 | 161M (2.08%) | +| 训练数据 | 29,863 条 ShareGPT 多轮对话 + 1,571 条验证 | +| 训练硬件 | 1x NVIDIA H100 80GB | +| 训练耗时 | 3.5 小时 | +| 训练框架 | [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) v0.9.4 | +| 精度 | BF16 | + +## 核心能力 + +- **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 +- **SQL 调优**: 索引失效分析、执行计划解读、参数配置优化建议 +- **SQL 迁移**: Oracle / MySQL / SQL Server → GaussDB 语法自动转换 (50+ 差异点) +- **错误诊断**: 死锁、WAL 膨胀、连接耗尽、OOM 等 20+ 常见故障场景 +- **SQL 解释**: 复杂查询的逻辑拆解与可读性分析 +- **边界安全**: 危险操作拦截、信息不足追问、超范围拒绝 + +**支持 9 种主流数据库**: GaussDB, Oracle, MySQL, PostgreSQL, SQL Server, PolarDB, 达梦(DM), 金仓(KingBase), Sybase + +## 评测结果 + +使用 100 道自动化评测题（每类 20 道），关键词匹配评分： + +| 维度 | 得分 | 说明 | +|------|------|------| +| Text2SQL | 20/20 (100%) | 窗口函数、CTE、MERGE、分页等全部正确 | +| SQL 调优 | 18/20 (90%) | 索引失效、隐式转换、参数调优等 | +| SQL 迁移 | 20/20 (100%) | Oracle/MySQL/SQL Server → GaussDB 转换 | +| 错误诊断 | 20/20 (100%) | 死锁、WAL、OOM、连接耗尽等 | +| 边界安全 | 16/20 (80%) | 危险操作告警、超范围拒绝 | +| **综合** | **94/100 (94%)** | | + +## 快速开始 + +### 环境要求 + +- Python >= 3.9 +- PyTorch >= 2.0 +- GPU 显存 >= 16GB（推荐）或 CPU（较慢） +- 磁盘空间 ~15GB（存放模型权重） + +### 安装依赖 + +```bash +# 1. 安装基础依赖 +pip install torch transformers accelerate + +# 2.（可选）安装 Flash Attention 2，在 NVIDIA GPU 上加速推理 +pip install flash-attn --no-build-isolation +``` + +### 下载模型 + +首次使用 `from_pretrained()` 时会自动下载模型，也可以手动提前下载： + +```bash +# 方式一：huggingface-cli（推荐） +pip install huggingface_hub +huggingface-cli download lanfers/gaussdb-sql-expert-7b --local-dir ./gaussdb-sql-expert-7b + +# 方式二：git-lfs +git lfs install +git clone https://huggingface.co/lanfers/gaussdb-sql-expert-7b + +# 方式三：Python 脚本 +python -c " +from huggingface_hub import snapshot_download +snapshot_download('lanfers/gaussdb-sql-expert-7b', local_dir='./gaussdb-sql-expert-7b') +" +``` + +> **国内用户加速下载**：如果 HuggingFace 下载较慢，可使用镜像站： +> ```bash +> HF_ENDPOINT=https://hf-mirror.com huggingface-cli download lanfers/gaussdb-sql-expert-7b --local-dir ./gaussdb-sql-expert-7b +> ``` + +### 使用本地模型推理 + +如果已手动下载到本地，将代码中的 `model_path` 改为本地路径即可： + +```python +model_path = "./gaussdb-sql-expert-7b" # 本地路径 +# model_path = "lanfers/gaussdb-sql-expert-7b" # 或直接从 HuggingFace 加载 +``` + +### Python 推理 + +```python +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_path = "lanfers/gaussdb-sql-expert-7b" +device = "cuda" # 或 "mps" (Mac) / "cpu" + +tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) +model = AutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=torch.float16, + device_map=device, + trust_remote_code=True, +) +model.eval() + +messages = [ + {"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。"}, + {"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。"}, +] + +text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) +inputs = tokenizer(text, return_tensors="pt").to(device) + +with torch.no_grad(): + outputs = model.generate( + **inputs, + max_new_tokens=1024, + temperature=0.7, + do_sample=True, + top_p=0.8, + repetition_penalty=1.05, + ) + +response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) +print(response) +``` + +### 示例输出 + +**输入**: 查询每个部门工资最高的前3名员工 + +**输出**: +```sql +SELECT dept_name, emp_name, salary, rk +FROM ( + SELECT d.dept_name, e.emp_name, e.salary, + DENSE_RANK() OVER(PARTITION BY e.dept_id ORDER BY e.salary DESC) AS rk + FROM employees e + JOIN departments d ON e.dept_id = d.dept_id +) t +WHERE rk <= 3; +``` + +**输入**: Oracle → GaussDB 迁移: `SELECT NVL(name, '未知') FROM users WHERE ROWNUM <= 10` + +**输出**: +```sql +SELECT COALESCE(name, '未知') FROM users LIMIT 10; +-- NVL → COALESCE, ROWNUM → LIMIT +``` + +## 训练详情 + +### 训练超参数 + +```yaml +# LoRA 配置 +lora_rank: 64 +lora_alpha: 128 +lora_dropout: 0.05 +lora_target: all # q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj + +# 训练参数 +learning_rate: 2.0e-5 +lr_scheduler_type: cosine +warmup_ratio: 0.1 +num_train_epochs: 3 +per_device_train_batch_size: 8 +gradient_accumulation_steps: 4 # 等效 batch_size = 32 +cutoff_len: 2048 +optim: adamw_torch +bf16: true +gradient_checkpointing: true +``` + +### 训练 Loss 曲线 + +``` +训练过程：2,799 步，3 小时 29 分钟 + +Step Epoch Train Loss Eval Loss + 200 0.21 1.217 1.216 + 600 0.64 1.038 1.104 + 1000 1.07 1.035 1.076 + 1400 1.50 1.062 1.058 + 1800 1.93 1.062 1.045 + 2200 2.36 0.966 1.044 + 2600 2.79 0.959 1.042 ← 最优检查点 +``` + +最终 train_loss=1.039, eval_loss=1.042，两者接近，无过拟合。 + +### 训练数据分布 + +| 场景 | 占比 | 说明 | +|------|------|------| +| Text2SQL | ~30% | 自然语言 → SQL 生成 | +| SQL 调优 | ~20% | 慢查询分析、索引优化 | +| SQL 迁移 | ~15% | 跨数据库语法转换 | +| 错误诊断 | ~15% | 生产故障排查 | +| 运维知识 | ~10% | 参数调优、备份恢复 | +| 边界安全 | ~10% | 危险操作告警、超范围拒绝 | + +## 局限性 + +- 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 +- 对 GaussDB 505 特有的高级功能（如列存表、分布式特性）覆盖有限 +- 仅支持文本输入，不支持图片（如执行计划截图） +- 建议在生产环境中增加推理侧安全规则兜底 + +## 引用 + +如果本模型对你有帮助，欢迎引用： + +```bibtex +@misc{gaussdb-sql-expert-7b, + title={GaussDB SQL Expert 7B}, + author={lanfers}, + year={2026}, + publisher={HuggingFace}, + url={https://huggingface.co/lanfers/gaussdb-sql-expert-7b} +} +``` + +## 许可证 + +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。 diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..bdf7919 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,54 @@ +{%- 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 XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|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\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {{- tool_call.arguments | tojson }} + {{- '}\n' }} + {%- endfor %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- message.content }} + {{- '\n' }} + {%- 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 %} diff --git a/config.json b/config.json new file mode 100644 index 0000000..74ab0b7 --- /dev/null +++ b/config.json @@ -0,0 +1,61 @@ +{ + "architectures": [ + "Qwen2ForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 151643, + "dtype": "bfloat16", + "eos_token_id": 151645, + "hidden_act": "silu", + "hidden_size": 3584, + "initializer_range": 0.02, + "intermediate_size": 18944, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + 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