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Our dedicated Gen AI team will help you fine tune an existing LLM or generative model based on your unique objectives.
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 generative model or 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 or generative model 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 data experts will review your model’s responses for accuracy, identify and highlight any errors, and rewrite responses to improve model performance, combining workflow automation with our human-in-the-loop approach to ensure speed and quality.
Our team can assess how well your Gen AI model understands, interprets, and executes instructions. We’ll help you identify where your model doesn’t comply, including why a response was selected. Any issues are highlighted and flagged, making it easier and more efficient to fine-tune.
Sama’s highly trained team of experts can help you improve the quality and alignment of model outputs through feedback loops, RLHF, and more. With domain expertise across multiple industries and functions, we can analyze and rank model responses, indicate the rationale behind each choice, and highlight any issues within the outputs.
Sama can help you scale captioning for a variety of modalities. Our team of experts will describe the content of visual inputs, verify if the captions match, and rewrite captions as needed to retrain the model to reduce errors and hallucinations. Sama’s proprietary platform makes sampling easy and our collaborative workflows help reduce subjectivity and ambiguity from project kickoff.
With domain expertise across a variety of industries and functions, Sama’s dedicated team can create new prompts and responses based on your model goals. We can also rewrite responses, tailored to model capabilities and limitations, to augment existing training data. Our team can also employ chain of thought to provide clear rationale for chosen outputs.
When real training data is too difficult or not cost effective to obtain, our team can create synthetic data sets to help train your model, using a human-in-the-loop approach to ensure the highest level of quality. Our team will define objectives for your data, including a specific domain or other required parameters, and test outputs for quality and accuracy by comparing them against outputs from authentic data.
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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Learn more about Sama's work with data curation
At Sama, we’ve developed tools that streamline this data curation process, ensuring every selected data sample aligns with your goals. Given the need for rapid experimentation and frequent configuration adjustments in our data curation pipeline - typically handled by an ML Applied Scientist - we leverage the Valohai platform to boost efficiency and reduce costs, all without requiring DevOps support.
Supervised fine-tuning takes a pre-trained LLM with general language knowledge and undergoes targeted improvement for a specific task. This involves feeding the LLM a curated dataset of labeled examples that connect desired outputs with the corresponding input data. By analyzing these labeled pairs, the LLM refines its understanding of the task and learns the specific patterns that lead to the desired outcome. This process essentially tailors the LLM's strengths to a particular domain, making it a more effective tool for that specific use case.
High-quality data, free from biases and factual errors, leads to more accurate and reliable outputs from the generative model. Additionally, a diverse range of training data, encompassing various styles, formats and viewpoints, equips the model to handle a wider range of prompts and scenarios effectively. This allows the new model to grasp the intricate connections between language patterns and desired outcomes, ultimately leading to a more accurate and effective fine-tuned model.
Prompt engineering is the process of creating the contextual instructions for your generative AI model. Prompt engineers don't just write generic instructions; they consider the model's capabilities, the specific task at hand, and the intended outcome. They might use clear and concise language, provide specific examples, or even break down complex tasks into smaller, easier-to-understand prompts.
Contextual optimization is crucial for supervised fine-tuning of LLMs because it bridges the gap between the general knowledge of the pre-trained model and the specific demands of the fine-tuning task. LLMs pre-trained on massive datasets can struggle to adapt to specific tasks without additional context. Contextual optimization provides that context by focusing the LLM on the nuances of the training data relevant to the task. This allows the fine-tuned model to perform well not just on the specific examples it saw during training, but also on unseen data that shares similar context.