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Model: ruohuaw/deepquery-3b-sft Source: Original Platform
2026-06-03 17:46:13 +08:00
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+---
+license: MIT
+
+frameworks:
+- Pytorch
+
+tasks:
+- text-generation
+
+model-type:
+- qwen2
+
+domain:
+- nlp
+
+language:
+- zh, en
+
+tags:
+- LoRA
+- GRPO
+- NL2SQL
+
+base_model:
+- Qwen/Qwen2.5-Coder-3B-Instruct
+
+base_model_relation: 
+- finetune
+
+datasets:
+- ruohuaw/sql-cot
+---
+notes
+-  we recommand turning off beam search and setting temperature = 0.1 for best performance
+
+dataset:
+- sql-cot: https://modelscope.cn/datasets/ruohuaw/sql-cot
+
+training logs:
+- https://wandb.ai/wangruohua1999econ-none/huggingface/runs/ygyz68bd?nw=nwuserwangruohua1999econ
+
+usage:
+```python
+import torch
+from modelscope import snapshot_download
+from modelscope import AutoTokenizer, AutoModelForCausalLM
+model_dir = snapshot_download('ruohuaw/deepquery-3b-sft')
+
+# 系统提示设置
+sys_prompt = """You are DeepQuery, a data science expert. Below, you are presented with a database schema, a question and a hint.Your task is to read the schema with annotations of the columns, understand the question and the hint, and generate a valid SQL query to answer the question. You should reason step by step, and includes your reasonings between <think> and </think>."""
+
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+model = AutoModelForCausalLM.from_pretrained(model_dir)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+model.eval()
+def generate_response(input_prompt, temperature = 0.1, max_length=512*4, model = model, tokenizer = tokenizer):
+    inputs = tokenizer(input_prompt, return_tensors="pt").to(device)
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_length = max_length,
+            temperature = temperature
+        )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+query = """Database Schema
+###
+CREATE TABLE Country
+(
+	CountryCode TEXT not null primary key,
+	LongName TEXT, -- `Long Name` description: long or full name of countries
+);
+
+CREATE TABLE Series
+(
+	SeriesCode TEXT not null primary key,
+);
+
+CREATE TABLE SeriesNotes
+(
+	Seriescode TEXT not null, -- `Series code` description: code identifying the series
+	Year TEXT not null, --
+	Description TEXT, --
+	primary key (Seriescode, Year),
+	foreign key (Seriescode) references Series(SeriesCode),
+);
+
+CREATE TABLE CountryNotes
+(
+	Countrycode TEXT NOT NULL, --
+	Seriescode TEXT NOT NULL, -- `Series code` description: Series code of countries
+	Description TEXT, --
+	primary key (Countrycode, Seriescode),
+	FOREIGN KEY (Seriescode) REFERENCES Series(SeriesCode),
+	FOREIGN KEY (Countrycode) REFERENCES Country(CountryCode),
+);
+
+###
+Question: 
+Please list the full names of any three countries that have their series code with a description of UN Energy Statistics (2014). 
+
+Hint:
+full name refers to longname
+"""
+input_prompt = tokenizer.apply_chat_template(
+                [
+                    {"role": "system", "content": SYS},
+                    {"role": "user", "content": QUERY},
+                    
+                ],
+                tokenize=False,
+                add_generation_prompt=True
+            )
+TRUE_ANSWER="""SELECT DISTINCT T2.LongName FROM CountryNotes AS T1 INNER JOIN Country AS T2 ON T1.Countrycode = T2.CountryCode WHERE T1.Description = 'Sources: UN Energy Statistics (2014)' LIMIT 3"""
+
+RESPONSE = generate_response(input_prompt)
+print(f"Response: \n{RESPONSE}\nTrue answer: \n{TRUE_ANSWER}")
+
+```
+
+SDK下载
+```bash
+#安装ModelScope
+pip install modelscope
+```
+
+```python
+#SDK模型下载
+from modelscope import snapshot_download
+model_dir = snapshot_download('ruohuaw/deepquery-3b-sft')
+```
+Git下载
+```
+#Git模型下载
+git clone https://www.modelscope.cn/ruohuaw/deepquery-3b-sft.git
+```