Fblgit
Models by this creator
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una-cybertron-7b-v2-bf16
116
The una-cybertron-7b-v2-bf16 model, developed by juanako.ai and maintained by fblgit, is a 7 billion parameter AI model that uses the UNA (Uniform Neural Alignment) technique. It outperforms other 7B models, scoring #1 on the HuggingFace Open LLM Leaderboard with a score of 69.67. Similar models include the Mistral-7B-v0.1, Intel/neural-chat-7b-v3-2, perlthoughts/Chupacabra-7B-v2, and fblgit/una-cybertron-7b-v1-fp16. Model inputs and outputs The una-cybertron-7b-v2-bf16 model is a text-to-text AI model, meaning it takes text as input and generates text as output. It performs well on a variety of natural language tasks, including question answering, logical reasoning, and open-ended conversation. Inputs Text prompts in natural language Outputs Generated text responses in natural language Capabilities The una-cybertron-7b-v2-bf16 model excels at mathematical and logical reasoning, scoring highly on benchmarks such as the HuggingFace Open LLM Leaderboard. It can engage in deep contextual analysis and provide detailed, well-reasoned responses. What can I use it for? The una-cybertron-7b-v2-bf16 model could be used for a wide range of natural language processing tasks, such as: Chatbots and conversational AI assistants Question answering and information retrieval Content generation for websites, blogs, or social media Summarization and text analysis Logical and mathematical problem-solving Things to try One interesting aspect of the una-cybertron-7b-v2-bf16 model is its use of the UNA (Uniform Neural Alignment) technique, which the maintainer claims helps "tame" the model. Experimenting with different prompts and tasks could reveal insights into how this technique affects the model's behavior and capabilities.
Updated 5/28/2024
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una-xaberius-34b-v1beta
84
The una-xaberius-34b-v1beta is an experimental 34B LLaMa-Yi-34B based model developed by juanako.ai. It was trained using Synthetic Fine-Tuning (SFT), Discriminative Pre-training Objective (DPO), and Uniform Neural Alignment (UNA) techniques on multiple datasets. This model outperformed the former leader tigerbot-70b-chat on the HuggingFace Open LLM Leaderboard, scoring 74.18 on average across various benchmarks. Model inputs and outputs The una-xaberius-34b-v1beta is a text-to-text model, capable of generating natural language outputs in response to input prompts. It can be used for a variety of tasks such as question answering, language generation, and text summarization. Inputs Natural language prompts and questions Outputs Generated natural language responses to the input prompts Capabilities The una-xaberius-34b-v1beta model has impressive capabilities, scoring highly on various benchmarks including MMLU, where it set a new record not just for 34B models but for all open-source LLMs. It is able to engage in deep reasoning and provide detailed, coherent responses. What can I use it for? The una-xaberius-34b-v1beta model could be useful for a wide range of applications that require natural language processing and generation, such as chatbots, virtual assistants, content creation, and knowledge-intensive tasks. However, as an experimental model, it's important to thoroughly evaluate its performance and safety before deploying it in production environments. Things to try One interesting aspect of the una-xaberius-34b-v1beta is the Uniform Neural Alignment (UNA) technique used in its training. This appears to be a new method developed by the maintainers, juanako.ai, that aims to "tame" language models. It would be worth exploring the details of this technique and how it affects the model's behavior and capabilities.
Updated 5/27/2024