una-cybertron-7b-v2-bf16
Maintainer: fblgit
116
🔍
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
📈
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 Invalid Date
👨🏫
neural-chat-7b-v3-3
71
The neural-chat-7b-v3-3 model is a fine-tuned 7B parameter large language model (LLM) from Intel. It was trained on the meta-math/MetaMathQA dataset and aligned using the Direct Performance Optimization (DPO) method with the Intel/orca_dpo_pairs dataset. The model was originally fine-tuned from the mistralai/Mistral-7B-v0.1 model. This model achieves state-of-the-art performance compared to similar 7B parameter models on various language tasks. Model inputs and outputs The neural-chat-7b-v3-3 model is a text-to-text transformer model that takes natural language text as input and generates natural language text as output. It can be used for a variety of language-related tasks such as question answering, dialogue, and summarization. Inputs Natural language text prompts Outputs Generated natural language text Capabilities The neural-chat-7b-v3-3 model demonstrates impressive performance on a wide range of language tasks, including question answering, dialogue, and summarization. It outperforms many similar-sized models on benchmarks such as the Open LLM Leaderboard, showcasing its strong capabilities in natural language understanding and generation. What can I use it for? The neural-chat-7b-v3-3 model can be used for a variety of language-related applications, such as building conversational AI assistants, generating helpful responses to user queries, summarizing long-form text, and more. Due to its strong performance on benchmarks, it could be a good starting point for developers looking to build high-quality language models for their projects. Things to try One interesting aspect of the neural-chat-7b-v3-3 model is its ability to handle long-form inputs and outputs, thanks to its 8192 token context length. This makes it well-suited for tasks that require reasoning over longer sequences, such as question answering or dialogue. You could try using the model to engage in extended conversations and see how it performs on tasks that require maintaining context over multiple turns. Additionally, the model's strong performance on mathematical reasoning tasks, as demonstrated by its results on the MetaMathQA dataset, suggests that it could be a useful tool for building applications that involve solving complex math problems. You could experiment with prompting the model to solve math-related tasks and see how it performs.
Updated Invalid Date
🤷
Wizard-Vicuna-7B-Uncensored
85
The Wizard-Vicuna-7B-Uncensored is a large language model developed by cognitivecomputations. It is based on the wizard-vicuna-13b model, but with a subset of the dataset - responses that contained alignment or moralizing were removed. The goal was to train a WizardLM that doesn't have alignment built-in, so that alignment can be added separately using techniques like RLHF LoRA. Similar models developed by the same maintainer include the Wizard-Vicuna-30B-Uncensored, WizardLM-30B-Uncensored, WizardLM-7B-Uncensored, and WizardLM-13B-Uncensored. These models share a similar intent of training a WizardLM without built-in alignment. Model Inputs and Outputs Inputs The Wizard-Vicuna-7B-Uncensored model accepts text inputs, which can be prompts or conversational inputs. Outputs The model generates text outputs, which can be used for a variety of language tasks such as summarization, text generation, and question answering. Capabilities The Wizard-Vicuna-7B-Uncensored model is capable of generating human-like text on a wide range of topics. It can be used for tasks like creative writing, dialogue generation, and task-oriented conversations. However, as an uncensored model, it lacks the safety guardrails that would prevent it from generating potentially harmful or biased content. What Can I Use It For? The Wizard-Vicuna-7B-Uncensored model could be used for experimental or research purposes, but great caution should be exercised when deploying it in production or public-facing applications. It may be better suited for individual use or closed-door experimentation, rather than public-facing applications. Potential use cases could include language model fine-tuning, dialogue systems research, or creative text generation, but the model's lack of safety filters means it should be used responsibly. Things to Try When working with the Wizard-Vicuna-7B-Uncensored model, it's important to carefully monitor the outputs and ensure they align with your intended use case. You may want to experiment with prompt engineering to steer the model's responses in a more controlled direction. Additionally, you could explore techniques like RLHF LoRA to add alignment and safety filters to the model, as mentioned in the model's description.
Updated Invalid Date
🏷️
SynthIA-7B-v1.3
142
The SynthIA-7B-v1.3 is a Mistral-7B-v0.1 model trained on Orca style datasets. It has been fine-tuned for instruction following as well as having long-form conversations. The model is released by migtissera under the Apache 2.0 license. Similar models include the neural-chat-7b-v3-1 and neural-chat-7b-v3-3 models, which are also fine-tuned 7B language models. However, the SynthIA-7B-v1.3 is focused on instruction following and open-ended conversations, rather than the more specialized tasks of those models. Model inputs and outputs Inputs Instruction**: The model accepts instructions or prompts for the AI assistant to elaborate on using a Tree of Thoughts and Chain of Thought reasoning. Outputs Natural language response**: The model generates a coherent, step-by-step response that addresses the given instruction or prompt. Capabilities The SynthIA-7B-v1.3 model demonstrates strong capabilities in open-ended instruction following and long-form conversation. It can break down complex topics, explore relevant sub-topics, and construct a clear reasoning to answer questions or address prompts. The model's performance is evaluated to be on par with other leading 7B language models. What can I use it for? The SynthIA-7B-v1.3 model would be well-suited for applications that require an AI assistant to engage in substantive, multi-turn dialogues. This could include virtual agents, chatbots, or question-answering systems that need to provide detailed, thoughtful responses. The model's ability to follow instructions and reason through problems makes it a good fit for educational or research applications as well. Things to try One interesting aspect of the SynthIA-7B-v1.3 model is its use of a "Tree of Thoughts" and "Chain of Thought" reasoning approach. You could experiment with prompts that ask the model to explicitly outline its step-by-step reasoning, exploring how it builds a logical flow of ideas to arrive at the final response. Additionally, you could test the model's ability to handle open-ended, multi-part instructions or prompts that require it to demonstrate flexible, contextual understanding.
Updated Invalid Date