SPEED-synthesis-7b-senior
Little Giants: Synthesizing High-Quality Embedding Data at Scale. Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou, arXiv 2024
This is the senior data synthesis model of SPEED.
Usage
Below is an example to synthesize classification data using this senior generator.
The prompts and misc scripts can be found in our github page
Transformers
import torch
import os
import random
import numpy as np
import json
import re
from torch import Tensor
from transformers import AutoTokenizer, AutoModelForCausalLM
from prompts_synthesis import get_create_classify_data_prompt
from utils import fix_common_json_errors_and_loads
LLAMA3_PROMPT = """
{prompt} [/INST]
""".strip("\n")
# Each query must come with a one-sentence instruction that describes the task
tasks = [
'Identify the intended age group for educational technology products.',
'Classify businesses based on their operational hours.'
]
language = 'English'
prompts = [LLAMA3_PROMPT.format(prompt=get_create_classify_data_prompt(task=task, language=language)[1]['content']) for task in tasks]
tokenizer = AutoTokenizer.from_pretrained('Haon-Chen/speed-synthesis-7b-senior')
model = AutoModelForCausalLM.from_pretrained('Haon-Chen/speed-synthesis-7b-senior')
model.to("cuda:0")
model.eval()
tokenizer.pad_token = tokenizer.pad_token or tokenizer.eos_token
tokenizer.padding_side = "left"
tokenizer.truncation_side = "left"
with torch.inference_mode():
# Tokenize the input texts
encodes = tokenizer(prompts, padding="longest", add_special_tokens=True, return_tensors="pt")
input_ids = encodes.input_ids.to(model.device)
attention_mask = encodes.attention_mask.to(model.device)
# Set the generation parameters
GEN_CONFIG = {"do_sample":True, "temperature": 1.0, "top_p": 1.0, "max_new_tokens": 800}
output = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
pad_token_id = tokenizer.eos_token_id,
**GEN_CONFIG
)
output_texts = tokenizer.batch_decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=False)
batch_results = []
for i in range(len(output_texts)):
batch_results.append(output_texts[i][len(prompts[i]):].strip(' '))
# Format outputs
bad_cnt=0
outputs = []
for i, result in enumerate(batch_results):
try:
output = fix_common_json_errors_and_loads(result)
user_query = output.get("input_text", "")
positive_document = output.get("label", "")
hard_negative_document = output.get("misleading_label", "")
except:
bad_cnt+=1
continue
out_data = {
"query": user_query,
"positives": [positive_document],
"negatives": [hard_negative_document],
"language": "English",
"task_definition": tasks[i],
}
outputs.append(out_data)
print(bad_cnt)
print(outputs)
Citation
If you find our paper or models helpful, please consider cite as follows:
@article{chen2024little,
title={Little Giants: Synthesizing High-Quality Embedding Data at Scale},
author={Chen, Haonan and Wang, Liang and Yang, Nan and Zhu, Yutao and Zhao, Ziliang and Wei, Furu and Dou, Zhicheng},
journal={arXiv preprint arXiv:2410.18634},
year={2024}
}