LLM Fine-Tuning Services

Stop struggling with LLM hallucinations and data gaps

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Trusted by 100+ Customers

ABB – ASEA Brown Boveri
Uipath
Yale
Bizerba
George Washington University
Toptal
Miami University
Hypatos
NEX
ABB – ASEA Brown Boveri
Uipath
Yale
Bizerba
George Washington University
Toptal
Miami University
Hypatos
NEX
ABB – ASEA Brown Boveri
Uipath
Yale
Bizerba
George Washington University
Toptal
Miami University
Hypatos
NEX

Use Cases for

Inference Calibration

Inference Calibration

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Optimize LLM for instruction adherence, error reduction, and style-specific responses

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Ensure hallucination-free interactions with a tailored tone

Content Moderation

Content Moderation

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Enable LLM to filter and remove undesirable social content

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Improve platform safety and compliance

Data Enrichment

Data Enrichment

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Add business-specific data and domain knowledge to your LLM

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Build custom models for industry use

Data Extraction

Data Extraction

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Train LLM to extract data from text documents

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Streamline document data handling

Discuss your challenges

Data Services for You

LLM Comparison

LLM Comparison

Question-Answer Pairs

Question-Answer Pairs

Prompts Generation

Prompts Generation

Image and Text Alignment

Image and Text Alignment

Image Captioning

Image Captioning

Object Detection and Classification

Object Detection and Classification

An example of 2 LLMs generating different inferences for fine tuner to choose the best response

Rating and comparing specific question-answer pairs of multiple LLMs.

An example of one LLM generating two different inferences for fine tuner to choose the best one

Creating tailored questions to fine-tune your LLM for specific use cases.

An example of a user submitting a detailed prompt with a detailed response from the LLM

Developing custom prompts to fine-tune the LLM for particular use cases.

An example of a user requesting the LLM to generate an image of a green truck. The LLM responses with the correctly generated image

Matching text descriptions accurately with corresponding images.

An example of a user requesting the LLM to depict the image. The LLM correctly guesses the description

Creating detailed captions for images and assessing the relevance of the generated text.

An image example of semantic segmentation

Train your LLM and Computer Vision models to annotate objects on images and text

LLM Comparison

LLM Comparison

svg
An example of 2 LLMs generating different inferences for fine tuner to choose the best response

Rating and comparing specific question-answer pairs of multiple LLMs.

Question-Answer Pairs

Question-Answer Pairs

svg
An example of one LLM generating two different inferences for fine tuner to choose the best one

Creating tailored questions to fine-tune your LLM for specific use cases.

Prompts Generation

Prompts Generation

svg
An example of a user submitting a detailed prompt with a detailed response from the LLM

Developing custom prompts to fine-tune the LLM for particular use cases.

Image and Text Alignment

Image and Text Alignment

svg
An example of a user requesting the LLM to generate an image of a green truck. The LLM responses with the correctly generated image

Matching text descriptions accurately with corresponding images.

Image Captioning

Image Captioning

svg
An example of a user requesting the LLM to depict the image. The LLM correctly guesses the description

Creating detailed captions for images and assessing the relevance of the generated text.

Object Detection and Classification

Object Detection and Classification

svg
An image example of semantic segmentation

Train your LLM and Computer Vision models to annotate objects on images and text

How It Works

Step 1

Free Pilot

Send us your LLM data sample for free fine-tuning to experience our services risk-free.

Step 2

QA

Evaluate the pilot results to ensure we meet your quality and cost expectations.

Step 3

Proposal

Receive a detailed proposal tailored to your specific LLM fine tuning needs.

Step 4

Start Labeling

Begin the fine-tuning process by our team.

Step 5

Delivery

Receive timely delivery of fine-tuned data, keeping your project on schedule.

Calculate Your Cost
Estimates line

1 Select pricing units
2 Specify how many objects to process
3 Check the approximate total cost
4 Run free pilot
Select Annotation Method
Amount
0 1M
Estimated Cost
$ x entities
$
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Send your sample data to get the precise cost FREE

Reviews

What Our Clients Say

Stars "We were impressed by the labeled data quality as well."

Label Your Data makes significant progress toward the goal of enhancing the client’s algorithm performance. The team works quickly to deliver annotations and annotators with extensive experience in the field. Their project management is straightforward, making for a smooth engagement.

Stars "They’re willing to drop everything to fulfill our needs and keep us happy."

Demonstrating a profound dedication to the project, Label Your Data consistently provides near-perfect deliverables at a cost-effective price. Thanks to their help, the client has been able to be more flexible with their work. Their impressive turnaround time further enhances the solid partnership.

Stars "Their flexibility and ability to work with multiple languages are impressive."

LYD showed high quality and flexibility and convinced us from day one with their high passion for delivering a great service. We currently evaluate further possibilities to further involve them in our initiatives and projects.

Stars “I’m impressed with how easy our communication with the team is and how simple and effective the processes are."

Label Your Data’s support has been crucial in developing the client’s computer vision perception algorithms.

Stars "The Label Your Data team was always available for questions."

Label Your Data provided the client with high-quality annotations and added to the number of annotations in the client’s portfolio. The team was consistently available for questions or updates that needed to be added to the data set.

Stars "They are flexible, dedicated, and their processes are well organized."

Label Your Data sends out weekly data annotation for the client to review. So far, the platform hasn’t had any issues and they are focusing on enhancing the platform since it was launched in November 2020. Their expertise shows productive results as the project progresses.

Why Projects Choose Label Your Data

No Commitment

No Commitment

Check our performance based on a free trial

Flexible Pricing

Flexible Pricing

Pay per fine-tuned object or per hour

Tool-Agnostic

Tool-Agnostic

Working with every fine-tuning tool, even your custom tools

Data Compliance

Data Compliance

Work with a data-certified vendor: PCI DSS Level 1, ISO:2700, GDPR, CCPA

Join Our Happy Customers

ABB – ASEA Brown Boveri
Uipath
Yale
Bizerba
George Washington University
Toptal
Miami University
Hypatos
NEX
ABB – ASEA Brown Boveri
Uipath
Yale
Bizerba
George Washington University
Toptal
Miami University
Hypatos
NEX
ABB – ASEA Brown Boveri
Uipath
Yale
Bizerba
George Washington University
Toptal
Miami University
Hypatos
NEX

Let’s Run the Free Pilot Together

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FAQs

What are the tools for LLM tuning?

Tools for LLM tuning include Hugging Face’s Transformers, OpenAI’s GPT-3 Playground, Google’s T5, and specialized platforms like Weights & Biases for experiment tracking. In addition, you can get custom scripts using PyTorch or TensorFlow for fine-tuning models on specific datasets.

What is instruction tuning LLM?

Instruction tuning LLM refers to the process of training language models to better understand and follow specific instructions provided by users. This improves their ability to generate accurate and contextually appropriate responses based on the given directives.

Why should I consider fine-tuning for my LLM?

Fine-tuning involves taking a pre-trained large language model (LLM) and further training it on a specific dataset. This improves its performance on a particular task. Besides, fine-tuning customizes your LLM to better understand and generate content relevant to specific use cases, making it more accurate and useful. This process can also significantly enhance the model’s ability to handle proprietary data and specific industry requirements.

What are the different methods of fine-tuning an LLM?

The primary methods of fine-tuning an LLM include:

Full Fine-Tuning: Retraining the entire model, which is resource-intensive but offers extensive customization.
Parameter Efficient Fine-Tuning (PEFT): Updating only a subset of model parameters to reduce computational cost, using techniques like LoRA (Low-Rank Adaptation).
Distillation: Training a smaller model to replicate the behavior of a larger one, making the process less resource-intensive and more efficient.

What are the benefits of fine-tuning over using a pre-trained model?

Fine-tuning offers several advantages. First, it tailors your model to boost its performance for specific tasks. Fine-tuning is also more cost-effective than training a model from scratch. Last but not least, you can get more relevant and accurate outputs of your LLM.

When should I consider fine-tuning an LLM?

You should consider fine-tuning an LLM when your engineering team needs accurate domain-specific terminology, or when operations managers require seamless integration with existing workflows. Fine-tuning enhances model precision, improves customer interactions, and automates tasks efficiently, providing tailored solutions and a competitive edge for your business.

How long does the LLM fine-tuning process take?

The duration of the fine-tuning process depends on various factors. They include the size and complexity of the model, the volume of training data, and the specific requirements of your project. On average, fine-tuning can take anywhere from a few days to a few weeks. Our team will provide a detailed timeline after assessing your needs and the available data.

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