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Our dedicated Gen AI team will help you fine tune an existing LLM based on your unique objectives.
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Fine-tuning an LLM for a specific domain, such as technical, medical, etc., requires a targeted approach. We help curate a high-quality dataset tailored to your domain, encompassing various aspects like tone, format and justifications. Our team can also evaluate and rewrite model responses for context and domain specificity to fine tune your model to its environment.
Our team will help drive the enhancement of RAG by fine-tuning question-answer pairs, incorporating retrieved passages from your knowledge base with proprietary documentation to inform answer generation and more. We’ll also evaluate models' outputs and rewrite any incorrect responses to create additional training data to better fine-tune your model.
We can help fine-tune models for specific tasks such as summarization or sentiment analysis. Our team starts by crafting clear and concise prompts along with corresponding answers. We’ll also evaluate and rewrite model responses based on your goals to help fine-tune an existing LLM to your exact needs.
Our team of LLM specialists can help fine-tune models to different writing styles such as creative, technical, formal, persuasive and more. We’ll review and rewrite prompts and corresponding responses based on the style of writing needed. For example, informative prompts that focus on conveying factual information will result in a more objective and neutral style while prompts that involve storytelling or imaginative elements will likely result in a more creative style.
Sama’s supervised fine-tuning projects start with tailored consultations to understand requirements for model behavior. This collaborative effort involves identifying key characteristics like tone, terminology, writing styles, relevant factual knowledge and more. We’ll align on how you want your model to behave and set targets across a variety of dimensions.
Our AI specialists leverage their expertise to write high quality prompts along with corresponding answers across varying formats and dimensions. We’ll curate a highly specialized set of data to help streamline the LLM development process.
After an initial set of data has been created we’ll work with your team to review the prompts and responses created to ensure the data aligns with the intended purpose of the generative model or LLM. If needed, our teams will collaborate closely to recalibrate.
As errors in model outputs are identified, our team will begin creating an additional training data set that can be used to fine-tune model performance based on your objective: domain specificity, task optimization, etc. This new data consists of rewritten prompts and corresponding responses that address the specific mistakes made by the model.
When the project is complete, we follow a structured delivery process to ensure smooth integration with your LLM training pipeline. We offer flexible and customizable delivery formats, APIs, and the option for custom API integrations to support rapid development of models.
With over 15 years of industry experience, Sama’s data annotation and validation solutions help you build more accurate GenAI models and LLMs—faster.
Our human-in-the-loop approach drives data-rich model improvements & RAG embedding enhancements through a variety of validation solutions. Our team provides iterative human feedback loops that score and rank prompts along with evaluating outputs. We also provide multi-modal captioning and sentiment analysis solutions to help models develop a nuanced understanding of user emotion and feedback.
We’ll help create new data sets that can be used to train or fine tune models to augment performance. If your model struggles with areas such as open Q&A, summarization or knowledge research, our team will help create unique, logical examples that can be used to train your model. We can also validate and reannotate poor model responses to create additional datasets for training.
Our team will help you build upon an existing LLM to create a proprietary model tailored to your specific needs. We’ll craft new prompts and responses, evaluate model outputs, and rewrite responses to improve accuracy and context optimization.
Our team of highly trained of ML engineers and applied data scientists crafts prompts designed to trick or exploit your model’s weaknesses. They also help expose vulnerabilities, including generating biased content, spreading misinformation, producing harmful outputs and more to improve the safety and reliability of your Gen AI models. This includes large scale testing, fairness evaluation, privacy assessments and compliance.
Our team is trained to provide comprehensive support across various modalities including text, image, and voice search applications. We help improve model accuracy and performance through a variety of solutions.
Our proactive approach minimizes delays while maintaining quality to help teams and models hit their milestones. All of our solutions are backed by SamaAssure™, the industry’s highest quality guarantee for Generative AI.
SamaIQ™ combines the expertise of the industry’s best specialists with deep industry knowledge and proprietary algorithms to deliver faster insights and reduce the likelihood of unwanted biases and other privacy or compliance vulnerabilities.
SamaHub™, our collaborative project space, is designed for enhanced communication. GenAI and LLM clients have access to collaboration workflows, self-service sampling and complete reporting to track their project’s progress.
We offer a variety of integration options, including APIs, CLIs, and webhooks that allow you to seamlessly connect our platform to your existing workflows. The Sama API is a powerful tool that allows you to programmatically query the status of projects, post new tasks to be done, receive results automatically, and more.
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Get models to market 3x faster by eliminating delays, missed deadlines and excessive rework
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Learn more about Sama's work with data curation
Large language models (LLMs) have emerged as powerful tools capable of generating human-like text, understanding complex queries, and performing a wide range of language-related tasks. Creating them from scratch however, can be costly and time consuming. Supervised fine-tuning has emerged as a way to take existing LLMs and hone them to a specific task or domain faster.
Sama provides ML Professionals and AI team Leads with an indispensable solution for Computer Vision data labeling.
