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Model: ruohuaw/deepquery-1.5b-rl
Source: Original Platform

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README.md

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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:
+- ruohuaw/deepquery-1.5b-sft
+
+base_model_relation: 
+- finetune
+
+datasets:
+- ruohuaw/sql-cot
+---
+
+dataset:
+- sql-cot: https://modelscope.cn/datasets/ruohuaw/sql-cot
+
+training logs:
+- GRPO reinforcement learning for Deepquery-v3-1.5b
+- https://wandb.ai/wangruohua1999econ-none/huggingface/runs/zt9wkj1i?nw=nwuserwangruohua1999econ
+
+notes
+-  we recommand turning off beam search and setting temperature = 0.1 for best performance
+
+usage:
+```python
+import torch
+from modelscope import snapshot_download
+from modelscope import AutoTokenizer, AutoModelForCausalLM
+model_dir = snapshot_download('ruohuaw/deepquery-1.5b-rl')
+
+# 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-1.5b-rl')
+```
+Git下载
+```
+#Git模型下载
+git clone https://www.modelscope.cn/ruohuaw/deepquery-1.5b-rl.git
+```

config.json

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+{
+  "_name_or_path": "./models/deepquery-v4-sft-1",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.48.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

configuration.json

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+{"task":"text-generation"}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.48.2"
+}

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+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "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
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

train.py

@@ -0,0 +1,207 @@
+from unsloth import FastLanguageModel, PatchFastRL
+PatchFastRL("GRPO", FastLanguageModel)
+from unsloth import is_bfloat16_supported
+import torch
+#---v4
+#pip install diffusers
+#!pip install "unsloth==2025.2.4" vllm
+#!pip install --upgrade pillow
+
+num_generations = 8
+per_device_train_batch_size = 8
+gradient_accumulation_steps = 4
+num_train_epochs = 3
+beta = 0.01
+learning_rate = 1e-4
+max_seq_length = 1650
+lora_rank = 8 
+lora_alpha = lora_rank * 2
+dataset_dir = "cot-qa-distill.csv"
+model_name = "./models/deepquery4-sft-1"
+output_dir = "./result/deepquery4-grpo-1"
+SYS="""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>."""
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = model_name,
+    max_seq_length = max_seq_length,
+    load_in_4bit = True, # False for LoRA 16bit
+    fast_inference = True, # Enable vLLM fast inference
+    max_lora_rank = lora_rank,
+    gpu_memory_utilization = 0.4, # Reduce if out of memory
+)
+tokenizer.pad_token = tokenizer.eos_token
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = lora_rank, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
+    target_modules = [
+        "gate_proj","up_proj", "down_proj","q_proj", "k_proj","o_proj", "v_proj"
+        #"q_proj", "k_proj", "up_proj", "down_proj",
+    ], # Remove QKVO if out of memory
+    lora_alpha = lora_alpha,
+    use_gradient_checkpointing = "unsloth", # Enable long context finetuning
+    random_state = 3407,
+)
+def extract_answer(text: str) -> str:
+
+    start_tag = 'My final answer is: \n```sql\n'
+    end_tag = '\n```'   
+    start_tag_index = text.find(start_tag)
+    if start_tag_index != -1:
+        start_index = start_tag_index + len(start_tag)
+        end_index = text.find(end_tag, start_index)
+        if end_index != -1:
+            return text[start_index:end_index].strip()
+
+    return ""
+
+from typing import List, Dict
+
+
+def sqlparser(left, right):
+    def format_sql(sql):
+        keywords = ['SELECT', 'FROM', 'WHERE', 'GROUP BY', 'ORDER BY', 'JOIN', 'INNER JOIN', 'HAVING']
+        formatted_sql = sql.upper()
+        for keyword in keywords:
+            formatted_sql = re.sub(rf'\b{keyword}\b', keyword, formatted_sql, flags=re.IGNORECASE)
+        return formatted_sql
+
+    left = format_sql(left)
+    right = format_sql(right)
+
+    components = {
+        'SELECT': r'SELECT\s+(.+?)\s+FROM',
+        'FROM': r'FROM\s+(.+?)(?=\s+(?:WHERE|GROUP BY|ORDER BY|JOIN|INNER JOIN|HAVING|$))',
+        'JOIN': r'(?:INNER )?JOIN\s+(.+?)\s+ON\s+(.+?)(?=\s*(?:WHERE|GROUP BY|ORDER BY|JOIN|INNER JOIN|HAVING|$))',
+        'WHERE': r'WHERE\s+(.+?)(?=\s+(?:GROUP BY|ORDER BY|HAVING|$))',
+        'GROUP BY': r'GROUP BY\s+(.+?)(?=\s+(?:ORDER BY|HAVING|$))',
+        'HAVING': r'HAVING\s+(.+?)(?=\s+(?:ORDER BY|$))',
+        'ORDER BY': r'ORDER BY\s+(.+)$'
+    }
+
+    def parse_component(sql, component, pattern):
+        if component == 'SELECT':
+            match = re.search(pattern, sql, re.IGNORECASE)
+            if match:
+                elements = match.group(1).split(',')
+                return set(element.strip() for element in elements)
+            return set()
+        elif component == 'JOIN':
+            joins = []
+            for match in re.finditer(
+                r'(?:INNER )?JOIN\s+(.+?)\s+ON\s+(.+?)(?=\s*(?:WHERE|GROUP BY|ORDER BY|JOIN|INNER JOIN|HAVING|$))', 
+                sql, 
+                re.IGNORECASE
+            ):
+                joins.append((match.group(1).strip(), match.group(2).strip()))
+            return joins
+        elif component in ['GROUP BY', 'ORDER BY']:
+            match = re.search(pattern, sql, re.IGNORECASE)
+            if match:
+                elements = match.group(1).split(',')
+                return set(element.strip() for element in elements)
+            return set()
+        else:
+            match = re.search(pattern, sql, re.IGNORECASE)
+            return match.group(1).strip() if match else ''
+
+    left_components = {}
+    right_components = {}
+    for component, pattern in components.items():
+        left_components[component] = parse_component(left, component, pattern)
+        right_components[component] = parse_component(right, component, pattern)
+
+    score = 0
+    total = 0
+    for component in components:
+        lc = left_components[component]
+        rc = right_components[component]
+        if lc or rc:
+            total += 1
+            if lc == rc:
+                score += 1
+    return score / total if total != 0 else 0
+def correctness_reward_func(prompts, completions, answer, **kwargs) -> list[float]:
+    responses = [completion[0]['content'] for completion in completions]
+    extracted_responses = [extract_answer(r) for r in responses]
+    return [sqlparser(a, r) for r, a in zip(extracted_responses, answer)]
+def bonus_reward_func(prompts, completions, answer, **kwargs) -> list[float]:
+    responses = [completion[0]['content'] for completion in completions]
+    extracted_responses = [extract_answer(r) for r in responses]
+    return [1.0 if r == a else 0.0 for r, a in zip(extracted_responses, answer)]
+
+def strict_format_reward_func(completions, **kwargs) -> list[float]:
+    """Reward function that checks if the completion has a specific format and minimum content length."""
+    pattern = r"^<think>(.*?)</think>.*"  
+    responses = [completion[0]["content"] for completion in completions]
+    rewards = []
+    for r in responses:
+        match = re.match(pattern, r, re.DOTALL)
+        if match:
+            reasoning = match.group(1)  
+            if len(reasoning.strip()) < 300:
+                rewards.append(0.0)
+            else:
+                rewards.append(0.1)
+        else:
+            rewards.append(0.0)
+    return rewards    
+from trl import GRPOConfig, GRPOTrainer
+training_args = GRPOConfig(
+    use_vllm = True, # use vLLM for fast inference
+    learning_rate = learning_rate,
+    adam_beta1 = 0.9,
+    adam_beta2 = 0.999,
+    weight_decay = 0.1,
+    warmup_ratio = 0.1,
+    beta=beta,
+    lr_scheduler_type = "cosine",
+    optim = "paged_adamw_8bit",
+    logging_steps = 1,
+    bf16 = is_bfloat16_supported(),
+    fp16 = not is_bfloat16_supported(),
+    per_device_train_batch_size = per_device_train_batch_size,
+    gradient_accumulation_steps = gradient_accumulation_steps, # Increase to 4 for smoother training
+    num_generations = num_generations, # Decrease if out of memory
+    max_prompt_length = max_seq_length,
+    max_completion_length = max_seq_length//2,
+    num_train_epochs = num_train_epochs, # Set to 1 for a full training run
+    save_steps = 100,
+    max_grad_norm = 0.5,
+    report_to = "wandb", # Can use Weights & Biases
+    output_dir = output_dir,
+)
+import re
+from datasets import load_dataset, Dataset
+import pandas as pd
+def dataset_process(name: str = dataset_dir) -> List[Dict]:
+    data = pd.read_csv(name)
+    processed_data = data.apply(lambda row: {
+        'prompt': [
+            {'role': 'system', 'content': SYS},
+            {'role': 'user', 'content': row['query']}
+        ],
+        'answer': row['answer']
+    }, axis=1).tolist()  
+    return processed_data
+    
+dataset = dataset_process()
+
+from swanlab.integration.transformers import SwanLabCallback
+swanlab_callback = SwanLabCallback(
+    project = "deepquery4-grpo",
+    experiment_name = "deepquery4-grpo-1",
+)
+
+trainer = GRPOTrainer(
+    model = model,
+    processing_class = tokenizer,
+    reward_funcs = [
+        strict_format_reward_func,
+        correctness_reward_func,
+        bonus_reward_func
+    ],
+    args = training_args,
+    train_dataset = dataset,
+    callbacks = [swanlab_callback]
+)
+trainer.train(resume_from_checkpoint = True)