StableVicuna Description
StableVicuna represents the inaugural large-scale open-source chatbot developed through reinforced learning from human feedback (RLHF). It is an advanced version of the Vicuna v0 13b model, which has undergone further instruction fine-tuning and RLHF training. To attain the impressive capabilities of StableVicuna, we use Vicuna as the foundational model and adhere to the established three-stage RLHF framework proposed by Steinnon et al. and Ouyang et al. Specifically, we perform additional training on the base Vicuna model with supervised fine-tuning (SFT), utilizing a blend of three distinct datasets. The first is the OpenAssistant Conversations Dataset (OASST1), which consists of 161,443 human-generated messages across 66,497 conversation trees in 35 languages. The second dataset is GPT4All Prompt Generations, encompassing 437,605 prompts paired with responses created by GPT-3.5 Turbo. Lastly, the Alpaca dataset features 52,000 instructions and demonstrations that were produced using OpenAI's text-davinci-003 model. This collective approach to training enhances the chatbot's ability to engage effectively in diverse conversational contexts.
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NetsPresso
NetsPresso serves as an advanced platform for optimizing AI models with a strong focus on hardware awareness. It facilitates on-device AI applications across various sectors, making it an essential tool for developing hardware-aware AI models. The incorporation of lightweight models like LLaMA and Vicuna allows for highly efficient text generation capabilities. Additionally, BK-SDM represents a streamlined version of Stable Diffusion models. Vision-Language Models (VLMs) effectively merge visual information with natural language processing. By addressing challenges associated with cloud and server-based AI solutions—such as limited connectivity, high expenses, and privacy concerns—NetsPresso stands out in the field. Furthermore, it operates as an automated model compression platform, effectively reducing the size of computer vision models to ensure they can function independently on smaller and less powerful edge devices. By optimizing target models through various compression techniques, the platform successfully minimizes AI models while maintaining their performance integrity. This dual focus on efficiency and effectiveness positions NetsPresso as a leader in the field of AI optimization.
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Vicuna
Vicuna-13B is an open-source conversational agent developed through the fine-tuning of LLaMA, utilizing a dataset of user-shared dialogues gathered from ShareGPT. Initial assessments, with GPT-4 serving as an evaluator, indicate that Vicuna-13B achieves over 90% of the quality exhibited by OpenAI's ChatGPT and Google Bard, and it surpasses other models such as LLaMA and Stanford Alpaca in more than 90% of instances. The entire training process for Vicuna-13B incurs an estimated expenditure of approximately $300. Additionally, the source code and model weights, along with an interactive demonstration, are made available for public access under non-commercial terms, fostering a collaborative environment for further development and exploration. This openness encourages innovation and enables users to experiment with the model's capabilities in diverse applications.
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Pricing
Pricing Starts At:
Free
Free Version:
Yes
Integrations
No Integrations at this time
Company Details
Company:
Stability AI
Year Founded:
2019
Headquarters:
United Kingdom
Website:
stability.ai/
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
StableVicuna Features and Options
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