Get High Quality Training
Data For
LLM Fine-tuning

Labellerr's advanced labeling platform help ML teams to setup Large Language Model finetuning process in few hours with custom workflow.
request a demo
No credit card required.
Get High Quality TrainingData For LLM Fine-tuning

An all-in-one Large Language Model fine-tuning platform
that's smart, simple and fast

Instruction Fine tuning

Train the model behavior based on specific instructions

Instruction Fine tuning

Reward model

Bring human evaluation and reinforcement learning to improve model behavior.
Multiple choice
Select the most suitable predefined option to the response.
Rating scale
Give rating to the output response based on accuracy, likability or fairness
Ranking the Output
Give ranking to the output response based on quality
Reward model

What is LLM ?

Model1 response

A specialized type of artificial intelligence (AI) that has been trained on vast amounts of text to understand existing content and generate original content.

What is LLM ?

What is LLM ?

Model2 response

A large language model (LLM) is a type of machine learning model that can perform a variety of natural language processing (NLP) task such as generating and classifying text, answering questions in a conversational manner, and translating text from one language to another

What is LLM ?
The scene describes a road through a jungle

The scene describes a road through a jungle

  • The caption is apt
    The caption is apt
  • The caption has digressed from the topic
    The caption has digressed from the topic
  • The caption dose not capture the overall essence
The scene describes a road through a jungle

A flexible design solution that
adapts to your workflow

Image/Video Models
Image/Video Models
  • Output Quality Ranking
  • Captioning Metadata
  • NSFW Content
  • Caption Classification
Large Language Models
Large Language Models
  • Summarization
  • Output Grading/Evaluation
  • Hallucinations
  • NSFW Content
LLM Comparison
LLM Comparison
  • Evaluate the model's accuracy on pre-defined metrices
  • Trained the reward model using RLHF

Fullfill The Data Need For Your Large Language Models

Data requirements for training large language models

LLMs are trained on a massive text corpus ranging at least in the size of 1000 GBs. The models trained on these datasets are very large, containing billions of parameters.

We must set up an infrastructure/hardware supporting multiple GPUs to train such large models on the massive text corpus.

Prepare large and diverse datasets for better language understanding and generation

Large and diverse datasets are essential for refining language models, enabling them to generalize across various linguistic patterns, styles, and cultural contexts.

These datasets enhance semantic understanding, capture real-world variability, and address biases, contributing to more accurate and contextually relevant language understanding and generation.

Learning in Large Language Models

What is transformer architecture?

The Transformer architecture is particularly proficient in processing sequential text data.

It receives a text sequence as input and generates another text sequence as output, exemplified in tasks like translating English sentences to Spanish. Fundamentally, it comprises a series of Encoder layers and Decoder layers at its core.

What is the attention mechanism in transformer architecture?

The attention mechanism in LLMs is a crucial component that enables the model to concentrate on distinct sections of the input text selectively.

This functionality assists the model in prioritizing the most pertinent portions of the input text, enhancing the precision of its predictions.

The Power of Large Language Models: Text Generation

Capabilities of LLMs in text generation

A large language model (LLM) is a deep learning algorithm adept at various natural language processing (NLP) tasks. Utilizing transformer models and trained on extensive datasets, these models are characterized by their substantial size.

This empowers them to excel in text recognition, translation, prediction, and content generation tasks.

Real-life examples of utilizing LLMs to solve problems

Large Language Models (LLMs), like FreedomGPT developed by CellStrat, revolutionize diverse applications. Enhancing search relevance, content generation, and market research, LLMs excel in tasks such as answering questions, aiding customer support, and legal analysis.

CellStrat offers end-to-end support, empowering businesses to leverage LLMs for transformative outcomes in a data-driven world.

Pre-training and Fine-tuning in Large Language Models

Pre-training phase

Large language models (LLMs) in NLP undergo pre-training, a phase where they are trained on extensive textual data without specific task instructions.

Unlike traditional machine learning that explicitly instructs models, pre-training allows LLMs to acquire general knowledge from vast datasets. Following pre-training, the models can be fine-tuned for specific tasks.

Fine-tuning LLMs to make them perform a specific task

The fine-tuning process is broadly divided into the following steps:
  • Begin with a foundational LLM (e.g., GPT-3).
  • Collect task-specific training data aligned with the intended use case.
  • Specify a training objective for the model (e.g., text classification, generation, or question answering).
  • Train the base model on new data and tasks by updating weights through backpropagation.
  • Evaluate performance on separate holdout data and make iterative adjustments.
  • Deploy the fine-tuned model for practical application.

Improving Data Efficiency in Large Language Models

How can one enhance the performance of Large Language Models by using limited annotated data?

There are various factors that can contribute to enhancing the performance of LLMs-

-Emphasizing clarity and precision over the data used,
-Contextual understanding,
-Temperature parameter adjustments,
-Frequency and presence penalties to reduce repetition,
-Strategic use of system messages,
-Prompt engineering,
-Model size considerations,
-Iterative refinement strategies,
-Addressing inaccuracies,
-Seeking human-like responses,
-Tailoring output length for precision.

What is the difference between transfer learning and semi-supervised learning?

Semi-supervised learning operates between supervised and unsupervised learning, utilizing a limited set of labeled samples alongside unlabeled ones to enhance task performance.

Transfer learning entails applying a solution from a known problem to a related new problem, often seen in Deep Learning where pretraining on datasets like imagenet precedes finetuning on the target problem's dataset for improved results.

Natural Language Understanding with LLMs

How are LLMs able to comprehend and interpret natural language inputs?

LLMs, or large language models, demonstrate an exceptional capacity to understand and interpret human language, unraveling context, extracting meaning, and discerning grammar nuances.

This advancement enables machines to respond to text in a manner more akin to human understanding. Unlike traditional language models that treat words in isolation, LLMs excel by considering the context in which words appear, creating adaptive word representations based on contextual information. This enhances the accuracy of language processing tasks significantly.

Real-life applications enabled by LLMs

LLMs possess versatile applications, with one prominent use being generative AI. ChatGPT, an accessible LLM, can generate diverse textual outputs, such as essays and poems, in response to prompts or questions.

LLMs, trained on extensive datasets like programming languages, aid programmers by crafting code snippets or completing programs. These models find utility in sentiment analysis, DNA research, customer service, chatbots, and online search. Examples of real-world LLMs include ChatGPT (OpenAI), Bard (Google), Llama (Meta), Bing Chat (Microsoft), and GitHub's Copilot, tailored for coding tasks.


What is Large Language Model?


Large language model are trained on large volume of datasets. It generally trained on 100Million + parameter. Initially it get trained on internet data, however to perform specific tasks like summarization, translation or text generation it requires fine tuning.

Is ChatGPT a large language model?


ChatGPT is an application based GPT3 large language model.

What is an example of a large language model?


All version of GPT, Llama, Bard are some of the most popular LLM.

Are Data Annotation Services Secure and Private?


Yes, all our service providers are security compliant.

What is the role of LLMs in chatbots ?


LLM helps building chatbot that can have the capability of generation natural language response. It gives chatbot capability to generate accurate response based on customer query.

Can LLMs be customized for specific industry needs?


Yes, one can achieve it by finetuning it, discuss your usecase with us.

Is LLM available for both research and commercial use?



What kind of support and assistance is available for users of Labellerr's LLM?


We help in finetuning the LLM by prividing expert-in-the-loop and preparing training data set that can be used as a prompt.

How can I get started with Labellerr's LLM for my specific project or business needs?


Discuss your use case with us, Reach out to us at

Build Vision/NLP/LLM Model Faster With 75% Less Cost

request a demo
Copyright © 2023 Tensor Matics, Inc. All right reserved.
Making AI journey simple!