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- Other Mall
- AI Guide
- AI Tutorial Series
- API Tutorial Series
- Other service tutorials
- 2025 New Product Guide


- Other Mall
- AI Guide
- AI Tutorial Series
- API Tutorial Series
- Other service tutorials
- 2025 New Product Guide
- …
- Other Mall
- AI Guide
- AI Tutorial Series
- API Tutorial Series
- Other service tutorials
- 2025 New Product Guide

LLM Training Data Mall
Neuronicx has professional data service experience and is a leading provider of high-quality LLM data and services. Whether you are building a basic model or need a customized enterprise solution, our experts are ready to support your specific AI needs throughout the project lifecycle.
LLM Data Package Mall ·Hot Data
Our AI data service team and technology have integrated and packaged the data, and you can purchase and use it directly.
Buy nowLLM Training Data (Mathematics 516G)HK$39,999.00HK$79,999.00Get high-quality original data, such as Chegg, Harvard University, etc.
Organize the raw data into a complete form and set the training strategy.
Process the data, including cleaning, labeling, conversion and training.
The data is evaluated, enhanced, and processed to form the final hit effect.
- 1
consult
Place an order on this page or contact our customer service for more information.
2Payment
After confirming the type of data you want to purchase, place an order by yourself.
3Shipping
After completing the purchase, the data package download link will be sent to your email. You can directly download and extract it from the download link.
The LLM lifecycle begins with curating diverse datasets to equip your models with relevant language and domain expertise. Developing base models and training LLMs for multimodal applications involves processing large amounts of raw data , including text, images, video, and audio, to help models effectively understand human language and various media types.
Data quality is the biggest differentiator when training large language models . Innovative AI requires carefully curated, high-quality datasets for a variety of applications. As the leading provider of AI training data, top trainers rely on Neuronicx to train and evaluate their models across different use cases, languages, and domain expertise.
Create prompts and responses tailored to different data needs to enhance model performance across different use cases and domains of expertise.
Supports diverse data needs, including:
- Different use cases: open QA, summarization, rewriting, chain reasoning, etc.
- Areas of Expertise: Subject matter expertise in areas such as mathematics, finance, coding, and healthcare.
- Multiple languages: Over 235 languages including English, Spanish and Japanese.
Leverage Neuronicx ’s AI Chat Feedback tool to enhance your models through Reinforcement Learning with Human Feedback (RLHF) and Direct Preference Optimization (DPO).
Key features:
- Supports custom workflow and training requirements
- Single or multi-turn conversation
- Customizable notes field
- Real-time human-computer interaction
The performance of the models was evaluated based on a series of LLM evaluation metrics such as relevance, accuracy, usefulness, and coherence.
Benefits include:
- Targeted insights into strengths and areas for improvement
- Compare different models during the development cycle through A/B testing
- Compare with competitors and other LLMs in the market
Leverage Neuronicx’s Red Team Crowd to proactively identify vulnerabilities and secure your LLM across different applications.
Conduct open-ended or targeted red team missions such as:
- Adversarial Attacks
- Category of harm (toxicity, bias, privacy, etc.)
- Multi-scenario based testing
- Guardrail test
- Review and comment on generated content
Tailor your model to a specific domain and generate more precise and contextual responses by bringing in a broader external knowledge base .
The Retrieval Enhancement Generation (RAG) data service includes:
- Data preparation: Collect, annotate, and manage datasets for your unique use case.
- Prompt dataset creation: Generate effective prompts for efficient model training.
- Evaluation and A/B testing: Compare the performance of different models and improve the output.
- Red Team: Stress test your models to proactively identify and address vulnerabilities.
User feedback
This is the feedback from our AI data center customers.
Llama 3 does not differ significantly from Llama2 in terms of model architecture. Our performance improvements are primarily due to improvements in data quality and diversity as well as larger training scales.
— Felix Zeltner (Technical Director, Mate)
Frontier threat red teams need to invest a lot of effort in exploring the underlying model capabilities. For us, the most important starting point is to work with domain experts with decades of experience.
— Darryl Duncan (AI Agency Staff)
As with previous GTP models , we use reinforcement learning with human feedback (RLHF) to fine-tune the model's behavior to produce responses that are more consistent with user intent.
— Alice Johnson (founder of AI company)
Contact Us
If you want to have a customized cooperation plan with us, please contact us!