Large Language models

Explore the future of LLMs through evaluations focused on human perspectives.

Refine and optimize best in class open-source Large Language Models (LLMs)

Deploy LLMs with assurance by employing evaluations that prioritize human insight.

Equip human experts with advanced tools to verify model results, including benchmarking, ranking, selection, named entity recognition, and classification features. Conduct evaluations on both pre-recorded and live chats, ensuring compatibility with extensive, multi-turn dialogues.

Enhance AI performance with RLHF and RLAIF.

Utilize the synergistic benefits of human evaluation paired with AI feedback. Create impeccable datasets by engaging internal experts and a proficient labeling team, specialized in RLHF, evaluative techniques, and red teaming. Achieve reliable, secure, and useful output with precision-curated datasets tailored for instructional tuning, RLHF, and supervised fine-tuning. Integrate AI-driven feedback with human oversight to effectively amplify model performance at scale while preserving high standards of quality.

Focus on essential user feedback to enhance LLMs.

Identify the most impactful feedback and cases using robust curation and discovery tools, including support for native vector and similarity searches.

Produce high-quality data for model alignment.

Secure reliable, safe, and beneficial results with precision datasets tailored for instructional tuning, Reinforcement Learning from Human Feedback (RLHF), and supervised fine-tuning. Prepare, label, and finalize datasets using a superior combination of human expertise and AI-assisted data processing.

Fine-tune LLMs using integrations with top-tier model providers.

Utilize advanced models from OpenAI, Cohere, and Anthropic, along with eminent open-source models, through the platform to facilitate smooth fine-tuning processes. Incorporate with Vertex AI, Databricks, and other prominent MLOps frameworks to initiate fine-tuning tasks directly within gradly.

Enhance your datasets and streamline routine operations using LLMs

Expedite data labeling and enrichment processes, enabling no-code data enhancement via premier closed-source and open-source large language models, saving time and reducing costs.

Advance LLMs with best data labeling expertise.

Utilize a cooperative platform for human feedback to produce impeccable datasets, bringing together in-house specialists and the foremost adept data labeling services specialized in Reinforcement Learning from Human Feedback (RLHF), assessment, and red teaming strategies.

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