8dd207761c
Model: BAAI_Industry_Competition_tourism_dev/eatbreakfast_TouInd Source: Original Platform
234 lines
7.0 KiB
Markdown
234 lines
7.0 KiB
Markdown
---
|
|
frameworks:
|
|
- Pytorch
|
|
license: MIT License
|
|
tasks:
|
|
- text-generation
|
|
|
|
#model-type:
|
|
##如 gpt、phi、llama、chatglm、baichuan 等
|
|
#- gpt
|
|
|
|
domain:
|
|
- nlp
|
|
|
|
#language:
|
|
##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
|
|
#- cn
|
|
|
|
#metrics:
|
|
##如 CIDEr、Blue、ROUGE 等
|
|
#- CIDEr
|
|
|
|
tags:
|
|
##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
|
|
- fine-tuned
|
|
|
|
#tools:
|
|
##如 vllm、fastchat、llamacpp、AdaSeq 等
|
|
#- vllm
|
|
---
|
|
## 模型推理结果文件
|
|
|
|
见`吃早饭_TouInd_12271627.jsonl`
|
|
|
|
## 训练和推理代码
|
|
|
|
### 训练脚本
|
|
|
|
```bash
|
|
# Experimental environment: V100, A10, 3090
|
|
CUDA_VISIBLE_DEVICES=0 \
|
|
swift sft \
|
|
--model_id_or_path Qwen/Qwen2.5-7B-Instruct \
|
|
--sft_type lora \
|
|
--use_dora True \
|
|
--tuner_backend peft \
|
|
--template_type AUTO \
|
|
--dtype AUTO \
|
|
--output_dir output \
|
|
--dataset qa_dataset_final3.jsonl sft_dataset_train_processed_rewritev2_with_example.jsonl \
|
|
--num_train_epochs 3 \
|
|
--max_length 2600 \
|
|
--check_dataset_strategy warning \
|
|
--lora_rank 8 \
|
|
--lora_alpha 32 \
|
|
--lora_dropout_p 0.05 \
|
|
--lora_target_modules ALL \
|
|
--gradient_checkpointing true \
|
|
--batch_size 1 \
|
|
--weight_decay 0.1 \
|
|
--learning_rate 5e-5 \
|
|
--gradient_accumulation_steps 16 \
|
|
--max_grad_norm 0.5 \
|
|
--warmup_ratio 0.03 \
|
|
--eval_steps 50 \
|
|
--save_steps 50 \
|
|
--save_total_limit 2 \
|
|
--logging_steps 10
|
|
```
|
|
|
|
### 推理代码
|
|
|
|
```python
|
|
import os
|
|
|
|
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
|
|
import gc
|
|
import hashlib
|
|
import json
|
|
|
|
import jsonlines
|
|
import torch
|
|
from swift.llm import (
|
|
ModelType,
|
|
get_default_template_type,
|
|
get_model_tokenizer,
|
|
get_template,
|
|
get_vllm_engine,
|
|
inference,
|
|
inference_vllm,
|
|
)
|
|
from swift.tuners import Swift
|
|
from swift.utils import seed_everything
|
|
from tqdm import tqdm
|
|
|
|
|
|
def generate_unique_code(input_string):
|
|
sha256_hash = hashlib.sha256()
|
|
sha256_hash.update(input_string.encode('utf-8'))
|
|
unique_code = sha256_hash.hexdigest()
|
|
return unique_code
|
|
|
|
def convert_config_to_dict(config_dict):
|
|
"""Convert generation config to JSON serializable format"""
|
|
serializable_dict = {}
|
|
for key, value in config_dict.items():
|
|
if isinstance(value, set):
|
|
serializable_dict[key] = list(value)
|
|
elif key == 'compile_config':
|
|
# Convert CompileConfig object to dict
|
|
serializable_dict[key] = {
|
|
'fullgraph': value.fullgraph,
|
|
'dynamic': value.dynamic,
|
|
'backend': value.backend,
|
|
'mode': value.mode,
|
|
'options': value.options
|
|
}
|
|
else:
|
|
serializable_dict[key] = value
|
|
return serializable_dict
|
|
|
|
seed_everything(42)
|
|
|
|
file_jsonl_path = "eval_only_query.jsonl"
|
|
out_path = "吃早饭_TouInd_12271627.jsonl"
|
|
log_file = "run_log.jsonl" # 推理日志
|
|
|
|
|
|
# for objective questions
|
|
model_type = ModelType.qwen2_5_7b_instruct
|
|
template_type = get_default_template_type(model_type)
|
|
model_id_or_path = None
|
|
llm_engine = get_vllm_engine(model_type,max_model_len=10000)
|
|
llm_engine.generation_config.max_new_tokens = 1024
|
|
llm_engine.generation_config.temperature = 0
|
|
llm_engine.generation_config.do_sample = False
|
|
|
|
template = get_template(template_type, llm_engine.hf_tokenizer)
|
|
|
|
reason_data = []
|
|
with open(file_jsonl_path, "rb") as file:
|
|
for line in jsonlines.Reader(file):
|
|
if line["query_type"]=="objective":
|
|
reason_data.append(
|
|
{
|
|
"query": line["query"] + "\n\nAnswer this multiple-choice question step by step. First, analyze each option carefully, explain why each is correct or incorrect, and then conclude with the best answer.\n",
|
|
"origin": line
|
|
}
|
|
)
|
|
resp_list = inference_vllm(llm_engine, template, reason_data, use_tqdm=True,max_batch_size=8,)
|
|
|
|
# Add run logging for first inference
|
|
for query_data, resp in zip(reason_data, resp_list):
|
|
run_info = {
|
|
"unique_id": generate_unique_code(query_data["query"]),
|
|
"model_inp": query_data["query"],
|
|
"gen_parm": convert_config_to_dict(llm_engine.generation_config.__dict__),
|
|
"answer": resp["response"]
|
|
}
|
|
with open(log_file, "a") as fw:
|
|
fw.write(json.dumps(run_info, ensure_ascii=False)+"\n")
|
|
|
|
final_data = []
|
|
for line in resp_list:
|
|
final_data.append(
|
|
{
|
|
"query": f"You are a good summarizer. Please summarize Answer and output the choise(s) like `A` or `AB` or `ACD` only.\n\n \n\nAnswer:{line['response']}\n\n",
|
|
}
|
|
)
|
|
# 修改max_new_tokens
|
|
resp_list = inference_vllm(llm_engine, template, final_data, use_tqdm=True,max_batch_size=8,)
|
|
|
|
# Add run logging for summarization inference
|
|
for data, resp in zip(final_data, resp_list):
|
|
run_info = {
|
|
"unique_id": generate_unique_code(data["query"]),
|
|
"model_inp": data["query"],
|
|
"gen_parm": convert_config_to_dict(llm_engine.generation_config.__dict__),
|
|
"answer": resp["response"]
|
|
}
|
|
with open(log_file, "a") as fw:
|
|
fw.write(json.dumps(run_info, ensure_ascii=False)+"\n")
|
|
|
|
with jsonlines.open(out_path, mode="a") as file_jsonl:
|
|
for origin,resp in zip(reason_data,resp_list):
|
|
file_jsonl.write({"query": origin["origin"]["query"], "query_type":origin["origin"]["query_type"],"answer": resp["response"]})
|
|
del llm_engine.model_executor
|
|
del llm_engine
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
torch.distributed.destroy_process_group()
|
|
import ray
|
|
|
|
ray.shutdown()
|
|
|
|
# for subjective questions
|
|
ckpt_dir = "BAAI_Industry_Competition_tourism_dev/eatbreakfast_TouInd"
|
|
model_type = ModelType.qwen2_5_7b_instruct
|
|
template_type = get_default_template_type(model_type)
|
|
model_id_or_path = None
|
|
model, tokenizer = get_model_tokenizer(model_type, model_id_or_path=ckpt_dir,
|
|
model_kwargs={'device_map': 'auto',"load_in_4bit":True})
|
|
# model = Swift.from_pretrained(model, ckpt_dir, inference_mode=True)
|
|
|
|
# 修改max_new_tokens
|
|
model.generation_config.max_new_tokens = 1024
|
|
model.generation_config.temperature = 0
|
|
model.generation_config.do_sample = False
|
|
template = get_template(template_type, tokenizer)
|
|
|
|
with open(file_jsonl_path,"rb") as file:
|
|
for i,line in tqdm(enumerate(jsonlines.Reader(file))):
|
|
if line["query_type"]=="subjective":
|
|
query = line['query']
|
|
response, history = inference(model, template, query)
|
|
|
|
# Add run logging for subjective questions
|
|
run_info = {
|
|
"unique_id": generate_unique_code(query),
|
|
"model_inp": query,
|
|
"gen_parm": convert_config_to_dict(model.generation_config.__dict__),
|
|
"answer": response
|
|
}
|
|
with open(log_file, "a") as fw:
|
|
fw.write(json.dumps(run_info, ensure_ascii=False)+"\n")
|
|
|
|
data = {"query":line["query"],"query_type":line["query_type"],"answer":response}
|
|
with jsonlines.open(out_path,mode="a") as file_jsonl:
|
|
file_jsonl.write(data)
|
|
```
|
|
|
|
|
|
|