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Model: ruohuaw/deepquery-1.5b-rl Source: Original Platform
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README.md
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README.md
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---
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license: MIT
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frameworks:
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- Pytorch
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tasks:
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- text-generation
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model-type:
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- qwen2
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domain:
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- nlp
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language:
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- zh, en
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tags:
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- LoRA
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- GRPO
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- NL2SQL
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base_model:
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- ruohuaw/deepquery-1.5b-sft
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base_model_relation:
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- finetune
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datasets:
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- ruohuaw/sql-cot
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---
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dataset:
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- sql-cot: https://modelscope.cn/datasets/ruohuaw/sql-cot
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training logs:
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- GRPO reinforcement learning for Deepquery-v3-1.5b
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- https://wandb.ai/wangruohua1999econ-none/huggingface/runs/zt9wkj1i?nw=nwuserwangruohua1999econ
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notes
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- we recommand turning off beam search and setting temperature = 0.1 for best performance
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usage:
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```python
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import torch
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from modelscope import snapshot_download
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from modelscope import AutoTokenizer, AutoModelForCausalLM
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model_dir = snapshot_download('ruohuaw/deepquery-1.5b-rl')
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# 系统提示设置
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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>."""
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tokenizer = AutoTokenizer.from_pretrained(model_dir)
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model = AutoModelForCausalLM.from_pretrained(model_dir)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = model.to(device)
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model.eval()
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def generate_response(input_prompt, temperature = 0.1, max_length=512*4, model = model, tokenizer = tokenizer):
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inputs = tokenizer(input_prompt, return_tensors="pt").to(device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_length = max_length,
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temperature = temperature
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return response
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query = """Database Schema
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###
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CREATE TABLE Country
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(
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CountryCode TEXT not null primary key,
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LongName TEXT, -- `Long Name` description: long or full name of countries
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);
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CREATE TABLE Series
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(
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SeriesCode TEXT not null primary key,
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);
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CREATE TABLE SeriesNotes
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(
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Seriescode TEXT not null, -- `Series code` description: code identifying the series
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Year TEXT not null, --
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Description TEXT, --
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primary key (Seriescode, Year),
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foreign key (Seriescode) references Series(SeriesCode),
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);
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CREATE TABLE CountryNotes
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(
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Countrycode TEXT NOT NULL, --
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Seriescode TEXT NOT NULL, -- `Series code` description: Series code of countries
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Description TEXT, --
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primary key (Countrycode, Seriescode),
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FOREIGN KEY (Seriescode) REFERENCES Series(SeriesCode),
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FOREIGN KEY (Countrycode) REFERENCES Country(CountryCode),
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);
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###
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Question:
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Please list the full names of any three countries that have their series code with a description of UN Energy Statistics (2014).
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Hint:
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full name refers to longname
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"""
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input_prompt = tokenizer.apply_chat_template(
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[
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{"role": "system", "content": SYS},
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{"role": "user", "content": QUERY},
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],
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tokenize=False,
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add_generation_prompt=True
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)
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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"""
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RESPONSE = generate_response(input_prompt)
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print(f"Response: \n{RESPONSE}\nTrue answer: \n{TRUE_ANSWER}")
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```
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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```
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('ruohuaw/deepquery-1.5b-rl')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/ruohuaw/deepquery-1.5b-rl.git
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```
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config.json
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config.json
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"torch_dtype": "float16",
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"transformers_version": "4.48.2",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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configuration.json
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configuration.json
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generation_config.json
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "4.48.2"
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}
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"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|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||
size 11421896
|
||||
208
tokenizer_config.json
Normal file
208
tokenizer_config.json
Normal file
@@ -0,0 +1,208 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"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|>",
|
||||
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|
||||
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|
||||
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
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|
||||
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|
||||
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|
||||
"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,
|
||||
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|
||||
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|
||||
"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|>",
|
||||
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|
||||
"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|>",
|
||||
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|
||||
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|
||||
"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
|
||||
}
|
||||
207
train.py
Normal file
207
train.py
Normal file
@@ -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)
|
||||
Reference in New Issue
Block a user