AI tools for nectar
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Nectar AI Companion
Nectar AI Companion is an innovative AI application that allows users to create and interact with their ideal AI girlfriend. Users can customize their AI companion's personality, appearance, and interests to suit their preferences. The application uses advanced AI algorithms to simulate realistic conversations and interactions, providing users with a unique and personalized virtual companion experience. With Nectar AI Companion, users can enjoy companionship, entertainment, and emotional support in a virtual setting.
Delibr
Delibr is a document-writing solution with generative AI baked in. Through interviewing and coaching over 500 product leaders, we've learned what's important and used it to train our AI. Delibr offers a range of AI-enhanced product templates, including PRDs, user personas, and strategy documents. Each template is designed to help you create high-quality documents that stand out. Delibr also includes an AI Copilot assistant that can review your documents, suggest impactful changes, and offer insights on comments. With Delibr, you can save time writing requirements, organize your ideas, and track your progress. Delibr is trusted by product teams around the world, including Storytel, Nectarine Health, RunaHR, and DecisionLink.
Nektar
Nektar is an AI-driven GTM automation platform that offers comprehensive control over customer data synchronization, including contacts, opportunity contact roles, GTM activities, and activity insights. It helps in matching sales processes and security needs efficiently. Trusted by high-performing global revenue teams, Nektar enables users to build more pipeline, win deals faster, and renew and expand customers. The platform leverages AI to transform buyer data at scale, providing visibility into buying groups, meeting quality, and contact roles. Nektar is designed to enhance customer success journeys, drive better renewal outcomes, and improve pipeline inspection using high-quality engagement data.
LLaMA-Factory
LLaMA Factory is a unified framework for fine-tuning 100+ large language models (LLMs) with various methods, including pre-training, supervised fine-tuning, reward modeling, PPO, DPO and ORPO. It features integrated algorithms like GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning, as well as practical tricks like FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA. LLaMA Factory provides experiment monitors like LlamaBoard, TensorBoard, Wandb, MLflow, etc., and supports faster inference with OpenAI-style API, Gradio UI and CLI with vLLM worker. Compared to ChatGLM's P-Tuning, LLaMA Factory's LoRA tuning offers up to 3.7 times faster training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory.
RLHF-Reward-Modeling
This repository contains code for training reward models for Deep Reinforcement Learning-based Reward-modulated Hierarchical Fine-tuning (DRL-based RLHF), Iterative Selection Fine-tuning (Rejection sampling fine-tuning), and iterative Decision Policy Optimization (DPO). The reward models are trained using a Bradley-Terry model based on the Gemma and Mistral language models. The resulting reward models achieve state-of-the-art performance on the RewardBench leaderboard for reward models with base models of up to 13B parameters.
awesome-synthetic-datasets
This repository focuses on organizing resources for building synthetic datasets using large language models. It covers important datasets, libraries, tools, tutorials, and papers related to synthetic data generation. The goal is to provide pragmatic and practical resources for individuals interested in creating synthetic datasets for machine learning applications.
LLamaTuner
LLamaTuner is a repository for the Efficient Finetuning of Quantized LLMs project, focusing on building and sharing instruction-following Chinese baichuan-7b/LLaMA/Pythia/GLM model tuning methods. The project enables training on a single Nvidia RTX-2080TI and RTX-3090 for multi-round chatbot training. It utilizes bitsandbytes for quantization and is integrated with Huggingface's PEFT and transformers libraries. The repository supports various models, training approaches, and datasets for supervised fine-tuning, LoRA, QLoRA, and more. It also provides tools for data preprocessing and offers models in the Hugging Face model hub for inference and finetuning. The project is licensed under Apache 2.0 and acknowledges contributions from various open-source contributors.
