From 86ee35b0cc9d77a6ecaa2cbac8e360cc2cf82eae Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 16 Aug 2026 19:59:12 +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: ruohuaw/deepquery-1.5b-rl Source: Original Platform --- .gitattributes | 49 +++++ README.md | 140 +++++++++++++ config.json | 29 +++ configuration.json | 1 + generation_config.json | 14 ++ model-00001-of-00002.safetensors | 3 + model-00002-of-00002.safetensors | 3 + model.safetensors.index.json | 345 +++++++++++++++++++++++++++++++ special_tokens_map.json | 31 +++ tokenizer.json | 3 + tokenizer_config.json | 208 +++++++++++++++++++ train.py | 207 +++++++++++++++++++ 12 files changed, 1033 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 config.json create mode 100644 configuration.json create mode 100644 generation_config.json create mode 100644 model-00001-of-00002.safetensors create mode 100644 model-00002-of-00002.safetensors create mode 100644 model.safetensors.index.json create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 train.py diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..21b3632 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,49 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 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 +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack 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 +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* 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 +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm 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 \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..763df20 --- /dev/null +++ b/README.md @@ -0,0 +1,140 @@ +--- +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 and .""" + +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 +``` \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..2cfd2cb --- /dev/null +++ b/config.json @@ -0,0 +1,29 @@ +{ + "_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 +} diff --git a/configuration.json b/configuration.json new file mode 100644 index 0000000..9fbcf95 --- /dev/null +++ b/configuration.json @@ -0,0 +1 @@ +{"task":"text-generation"} \ No newline at end of file diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..3dec53a --- /dev/null +++ b/generation_config.json @@ -0,0 +1,14 @@ +{ + "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, + 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"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 XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\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\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n' }}\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\\n' }}\n {{- message.content }}\n {{- '\\n' }}\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 +} diff --git a/train.py b/train.py new file mode 100644 index 0000000..e51fc17 --- /dev/null +++ b/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 and .""" + +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"^(.*?).*" + 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) \ No newline at end of file