Customizing Data to Train Large Language Models: LLM Training Data Agents

The hidden wonders of proxy IP in data training

Those of you who are involved in machine learning know that data is like an ingredient in a stir-fry. But what many people don't realize is thatAccess to raw materialsDirectly affect the flavor of the final dish. To cite a real case: last year, a team wanted to train customer service robots, directly grabbed a forum for three years of posts, the results of the model just on-line was complained about discriminatory terminology - it turned out that the forum is mixed with a large number of accounts.

At this point if you use ipipgo's dynamic residential proxy, the situation is very different. Their real residential IPs can bypass the platform's anti-crawl mechanism by setting up request intervals like this:

import requests
from itertools import cycle

proxy_pool = cycle(ipipgo.get_proxy_list()) Get Dynamic IP Pools

for page in range(1, 100): proxy = next(proxy_pool): proxy = next(ipipgo.get_proxy_list)
    proxy = next(proxy_pool)
    res = requests.get(f "https://example.com/page/{page}", proxies={"http_pool")
                      proxies={"http": proxy, "https": proxy})
     Processing data logic...

watch carefullyThe cycle function in line 4This is the key to realizing automatic IP rotation. ipipgo's API supports automatic switching, which is much more trouble-free than manual management. The last time I helped a friend tune this, the collection efficiency directly doubled not to mention that the probability of being sealed from 30% down to less than 3%.

The Three Pitfalls of Data Collection and the Way to Crack Them

Seen too many people fall into these three pits:

The third question in particular suggests that the code should be added with aFailure Retry Mechanism. Last time there was a customer doing e-commerce price comparison, the data integrity rate soared from 72% to 98% after using this method:

def safe_request(url): for _ in range(3): maximum 3 retries
    for _ in range(3): retry at most 3 times
        try: proxy = ipipgo.get_random_proxy()
            proxy = ipipgo.get_random_proxy()
            return requests.get(url, proxies=proxy, timeout=10)
        except Exception as e.
            ipipgo.report_failed(proxy) mark IP as failed
    return None

Practical: Building an Exclusive Corpus

Say a real operation process. An AI startup wants to train industry pendant models, and took care of data collection by following this step:

1. With ipipgo.City-level location agentsCapture local forums (dialects vary greatly from city to city)
2. Start 10 docker containers to collect in parallel, each bound to a separate IP
3. Setting up centralized collection from 2-5am (during idle bandwidth period of target websites)
4. Automatic weekly update of 10% of data volume

The key is toSimulates the rhythm of human operation. There's a tricky way to do this: add a random wait time to the request interval, like this:

import random
import time

def human_delay():
    base = 1.2 base wait time
    variation = random.uniform(-0.3, 0.8) random fluctuations
    time.sleep(max(0.5, base + variation)) no less than 0.5 seconds

Frequently Asked Questions QA

Q: What should I do if I always encounter CAPTCHA when collecting?
A: A combination of three approaches: 1) Reduce the frequency of individual IP requests 2) Enable ipipgo's highly anonymized proxies 3) Insert manual operations at key nodes

Q: Does the training data need to be cleaned?
A: It has to be! Seen the most exaggerated case of phishing site content mixed in with the raw data. It is recommended to do at least three layers of filtering: sensitive words, semantic integrity, information density

Q: What are the special advantages of ipipgo?
A: Their homeBusiness Scenario Customization ServiceIt's a real flavor. Last time there was a project that required a specific carrier IP, no one else could do it, they got the exclusive channel done in three days.

Finally, a piece of cold knowledge: models trained with proxy IPs perform better when dealing with geographical language features. Because the geographic distribution of the data source is closer to the real user situation, this detail is overlooked by many teams. Next time before starting a training task, remember to check whether your IP pool configuration is reasonable.