The most experienced foundation LLM fine-tuning company
Enhance model performance with LLM fine-tuning
Accelerate your model reasoning and coding capabilities with Turing's LLM fine-tuning services using proprietary human data for supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and direct preference optimization (DPO).
Leading LLM companies and research organizations have trusted Turing
Human data makes the difference
When fine-tuning LLMs, high-quality human data is the secret to success. Turing uses a multi-point model measurement, improvement, and enhancement methodology centered on real, proprietary human data—optimizing your LLM coding, multimodal reasoning, and more.
LLM fine-tuning and data specialties
For AI accelerators creating bespoke LLMs or Fortune 500 enterprises deploying AGI solutions, Turing's LLM fine-tuning expertise enhances your models with advanced techniques and domain-specific insights.
Supervised Fine-Tuning (SFT)
Reinforcement Learning from Human Feedback (RLHF)
Direct Preference Optimization (DPO)
Domain-specific fine-tuning
High-quality LLM fine-tuning starts here
Ready for superior model fine-tuning solutions?
Model evaluation and analysis
Our in-house solutions architects and experts help you scope your project for task complexity, volume, and effort.
Fully-managed training team assembly
Using our vetted technical professionals, we build your fully managed team of model trainers and more—with additional customized vetting, if necessary.
Large language model data and training tasking
You focus solely on task design while we handle coordination and operation of your dedicated training team.
Scale on demand
Maintain consistent quality control with iterative workflow adaptation and agility as your training needs change.
Ready for superior model fine-tuning solutions?
Talk to one of our solutions architects and start your LLM fine-tuning and enhancement project.
Cost-efficient R&D for LLM training and development
Empower your research teams without sacrificing your budget or business goals. Get our starter guide on strategic use, development of minimum viable models, and prompt engineering for a variety of applications.
“Turing’s ability to rapidly scale up global technical talent to help produce the training data for our LLMs has been impressive. Their operational expertise allowed us to see consistent model improvement, even with all of the bespoke data collection needs we have.”
Want the highest-quality LLM fine-tuning?
Talk to one of our solutions architects and start your LLM fine-tuning project.
Frequently asked questions
Find answers to common questions about fine-tuning and enhancing high-quality LLMs.
What is an LLM and how can it benefit your business?
Large language models (LLMs) are highly sophisticated artificial intelligence programs designed to understand, interpret, and generate humanlike text by processing vast datasets. Moreover, today's advanced multimodal LLMs can simultaneously process and generate multiple types of data, such as text, images, and sounds.
Here's how LLMs can benefit different business aspects:
- Customer support: LLMs empower businesses to automate routine customer inquiries, deliver personalized responses, and enhance the overall efficiency and effectiveness of support services. This leads to faster resolutions and a more personalized customer experience.
- Marketing automation: LLMs streamline content creation and personalize customer communications, allowing for data-driven, targeted marketing campaigns that compel engagement and conversions.
- Business analytics: By integrating LLMs into analytical processes, businesses can process complex datasets faster to automate report generation and provide actionable insights that drive strategic decisions.
- Personalization and recommendation systems: LLMs can digest customer data to deliver personalized recommendations and experiences across multiple platforms, increasing engagement, conversions, and customer loyalty.
- Application development: LLMs support developers with intelligent code suggestions and error identification that simplify the development process, reduce bugs, and accelerate the production of high-quality applications.
What are the ethical considerations with using large language models?
Some of the ethical aspects that businesses should consider when incorporating LLMs into their operations include:
- Data privacy and security: LLMs process and learn from vast datasets that may contain sensitive information. Ensuring this data is handled with the utmost security and complies with privacy regulations like GDPR and CCPA is essential.
- Bias and fairness: Since LLMs develop their understanding from existing data, they can pick up and perpetuate any biases present in that data. It's important to audit training datasets and continuously monitor model outputs to identify and mitigate biases.
- Misinformation and abuse: LLMs could be used to generate false or misleading content, such as deepfakes or fake news. Developing robust authentication mechanisms and promoting the ethical use of technology are steps toward minimizing this risk.
- Transparency and accountability: It's important for businesses to be transparent about how they use LLMs and to establish clear lines of accountability for the outcomes produced by these models.
- Environmental impact: Training large models can require significant computational resources and energy, raising concerns about the environmental footprint of AI development. Optimizing computer architectures and efficiency are ongoing endeavors to address this issue.
What kind of LLM fine-tuning services does Turing offer?
Turing offers a comprehensive suite of LLM fine-tuning services designed to optimize your models for diverse use cases, ensuring high performance, alignment, and domain-specific expertise. Our services include:
- Supervised Fine-Tuning (SFT):
Enhance your LLM’s performance with curated, high-quality datasets tailored to specific business objectives. Our SFT services ensure precise outputs aligned with your needs. - Reinforcement Learning from Human Feedback (RLHF):
Leverage human feedback to train models for ethical, fair, and contextually relevant behavior. RLHF helps refine model decision-making and reduces harmful outputs. - Direct Preference Optimization (DPO):
Align your LLM’s behavior with user preferences and business goals, ensuring outputs that are both relevant and actionable. - Domain-Specific Fine-Tuning:
Equip your LLM with specialized expertise in areas such as STEM, healthcare, finance, or other industries.
How do I get started with implementing LLMs in my organization?
Kickstart your venture into LLMs by allocating a small portion of your R&D budget—about 2–5 %—to trial LLM initiatives with the potential for high returns. Don't hesitate to start small-scale experiments to discover where LLMs can add the most value to your business.
For seamless LLM integration and execution, consider partnering with a company like Turing, a leading LLM training services provider. With our proven track record of helping top foundational LLM companies in reasoning and coding, data generation, and model fine-tuning, we'll help you navigate the complexities of LLM adoption.
Why choose Turing for LLM fine-tuning?
- Comprehensive training expertise: With experience across SFT, RLHF, and DPO cycles, Turing ensures your LLMs deliver high-quality results.
- Proprietary human data: Our datasets drive unmatched precision in training, tailored to your business needs.
- Scalable and flexible solutions: From small-scale testing to enterprise-level deployment, our services adapt to your evolving requirements.
- Domain-specific knowledge: Whether it’s STEM, coding, or industry-specific insights, Turing equips your models with the expertise they need.