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Prompt Training

LLM Fine-Tuning: Models for Precision and Relevance

Fine-tuning Language Models (LLMs) is crucial for adapting general AI systems to meet specific task requirements. By customizing LLMs, you can achieve precise and relevant performance, whether in customer support, content generation, or specialized NLP applications.

Humans are the key for great LLMs
How the Fine-Tuning process always requires humans?

1.
Why Fine-Tuning Matters for LLMs?

Fine-tuning transforms pre-trained language models, enabling them to excel in specific domains. Through the adjustment of parameters and the integration of domain-specific data, this process significantly enhances model accuracy, reduces bias, and ensures output relevance.

2.
The Fine-Tuning Process Explained

The fine-tuning process involves refining a pre-trained LLM using task-specific datasets. This includes steps like data preparation, prompt engineering, and iterative model testing, often leveraging Human-in-the-Loop (HITL) and Reinforcement Learning from Human Feedback (RLHF) to achieve optimal results.

3.
Benefits of Fine-Tuning for Specific Tasks

Fine-tuning delivers numerous benefits, including improved task-specific performance, enhanced accuracy, reduced bias, and output tailored to your business needs. These customizations make your AI systems more effective and aligned with your objectives.

Data Labeling Workflows built with Ethics.
Everything you need to get your data perfectly labeled, from prompt engineering to model evaluation.

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& GenAI tools
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TAILORED DATA LABELING WORKFLOWS
We ensure successful data labeling with tailored workflows.
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AGILE PROJECT MANAGEMENT TEAM
We ensure efficiency with dedicated Data Experts.
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SOLID HUMAN-IN-THE-LOOP
Working with a diverse, qualified and trained labelers.
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INTEGRATED

API integration to your own systems.

PAY-AS-YOU-GO

Competitive market pricing solutions.

Customized

Tailored workforce from all over the world.

EthicAL

Social impact controlled and measured.
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Diverse Applications of Fine-Tuned LLMs

Explore how fine-tuned LLMs can revolutionize specialized tasks across various industries, delivering precision and tailored performance.

Data Collection & model creation
REINFORCEMENT LEARNING FROM HUMAN FEEDBACK (RLHF)
CONTENT MODERATION
REAL-TIME SUPPORT

DATA 
COLLECTION AND MODEL CREATION

  • Gather, create, or curate prompt-generation for your Generative AI model

  • Ask Data experts to enhance model accuracy

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RLHF

  • Evaluate and rank prompts and results

  • Get human feedback to score and categorize responses

  • Conduct model evaluations to refine performance

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CONTENT MODERATION

  • Identify and remove negative generated content

  • Review prompts and outputs for potential issues, with adversarial testing

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REAL-TIME SUPPORT

  • Provide real-time support for Generative AI models in production

  • Conduct ongoing human verification and confirmation for classifier support

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Why choosing isahit?
Leverage skilled on-demand workforce for ethical AI projects using top AI tools.

THE Only
ETHICAL
CHOICE

We place impact at the heart of our business model and measure it every year, making us the first B Corp certified AI company in Europe.

THE MOST DIVERSIFIED WORKFORCE

Our workforce is multicultural, coming from 44 different countries, speaking more than 16 languages, with different academic backgrounds and professional experiences.

A HIGHLY
TRAINED WORKFORCE

We assign our workforce to projects based on their skills and then provide them with a complete onboarding (more than 3 hours of training per project) & ongoing coaching.

THE MOST
AGILITY SOLUTIONS

We understand our customer's needs for flexibility and offer them appropriate solutions: scalable workforce, tools for every labelling needs, pay as you go system.

Our Customers
We helped them getting clean datasets

USE CASE : L'Oréal

Discover how L'Oréal uses our image annotation service to train their facial recognition algorithm and capitalise on the diversity of our workforce to avoid including biases in their models.

  • Use of a consensus process

  • Assigning images according to the skin type : (Indian, Asian, African, American, Caucasian)

  • Order of points

USE CASE : Airbus

Find out how Airbus uses our image annotation service to train its recognition algorithms on satellite imagery and capitalises on the flexibility of our service for mass annotations on an ad hoc basis.

  • Process of managing fluctuating image flows

  • Optimisation of the tool to handle several hundred annotations per image

  • Use of the directional bounding box with directional vector

USE CASE : Sodexo

Come and see how Sodexo uses our image annotation service to train their Food Recognition algorithm and capitalise on the diversity of our workforce to avoid bias in their models.

  • A tailor-made annotation pipeline

  • A tailor-made API

  • Specific interface for label management

Optimize Your Language Models Today
Contact us to discover how our fine-tuning services can enhance your LLMs for task-specific precision.

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