Senku-70B-Full
Maintainer: ShinojiResearch
139
🌐
| 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
Senku-70B-Full is a large language model developed by ShinojiResearch, a team of AI researchers and engineers. This model is a fine-tuned version of the 152334H/miqu-1-70b-sf model, which was originally trained on a synthesized Wikipedia conversation dataset. The fine-tuning process utilized the Slimorca dataset and a custom LoRA adapter to achieve state-of-the-art performance on several benchmark tasks.
Compared to similar models like neural-chat-7b-v3-3 and 7B, Senku-70B-Full boasts impressive capabilities across a range of domains, including text generation, question answering, and commonsense reasoning.
Model inputs and outputs
Inputs
- Raw text prompts that can be used to guide the model's generation, such as instructions, queries, or dialogue contexts.
Outputs
- Fluent, coherent text continuations that align with the provided input prompt.
- Responses to questions or information requests.
- Logical inferences and explanations based on the input context.
Capabilities
The Senku-70B-Full model has demonstrated strong performance on a variety of benchmark tasks, including the EQ-Bench, GSM8k, and Hellaswag. It can engage in thoughtful, contextually-appropriate dialogue, offer insightful analysis and commentary, and tackle complex reasoning problems. The model's broad knowledge and language understanding make it suitable for use in a wide range of applications, from chatbots and virtual assistants to content generation and question-answering systems.
What can I use it for?
With its impressive capabilities, the Senku-70B-Full model can be leveraged for a variety of applications, such as:
- Building conversational AI assistants that can engage in natural, informative dialogue
- Generating high-quality written content, such as articles, stories, or scripts
- Powering question-answering systems that can provide accurate and detailed responses
- Enhancing search and recommendation engines with advanced language understanding
- Enabling more sophisticated and personalized interactions in customer service and support applications
Things to try
One interesting aspect of the Senku-70B-Full model is its ability to adapt to different prompt formats, such as the ChatML template used in the neural-chat-7b-v3-3 model. Experimenting with various prompt styles and structures can help you unlock the model's full potential and find the most effective way to leverage its capabilities for your specific use case.
Additionally, you may want to explore the model's performance on different types of tasks, such as creative writing, code generation, or multi-turn dialogue, to better understand its strengths and limitations. Comparing its outputs and behavior to other large language models can also provide valuable insights.
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
🖼️
Kunoichi-DPO-v2-7B
65
The Kunoichi-DPO-v2-7B model is a powerful general-purpose AI model developed by SanjiWatsuki. It is an evolution of the previous Kunoichi-7B model, with improvements in intelligence and performance across various benchmarks. The Kunoichi-DPO-v2-7B model achieves strong results on key benchmarks like MT Bench, EQ Bench, MMLU, and Logic Test, outperforming many other models in its size range, including GPT-4-Turbo, GPT-4, and Mixtral-8x7B-Instruct. It also performs well on other evaluations like AGIEval, GPT4All, TruthfulQA, and BigBench. Model inputs and outputs Inputs Text inputs, typically in the form of plain natural language prompts Outputs Text outputs, in the form of generated responses to the provided prompts Capabilities The Kunoichi-DPO-v2-7B model is a highly capable general-purpose AI system. It can engage in a wide variety of tasks, including natural language processing, question answering, creative writing, and problem-solving. The model's strong performance on benchmarks like MT Bench, EQ Bench, and MMLU suggests it has strong language understanding and reasoning abilities. What can I use it for? The Kunoichi-DPO-v2-7B model can be used for a wide range of applications, from content generation and creative writing to task assistance and research support. Potential use cases include: Helping with research and analysis by summarizing key points, generating literature reviews, and answering questions Assisting with creative projects like story writing, poetry generation, and dialogue creation Providing task assistance and answering queries on a variety of topics Engaging in open-ended conversations and roleplay Things to try One interesting aspect of the Kunoichi-DPO-v2-7B model is its strong performance on the Logic Test benchmark, which suggests it has robust logical reasoning capabilities. Users could try prompting the model with logical puzzles or hypothetical scenarios to see how it responds. Additionally, the model's high scores on benchmarks like EQ Bench and TruthfulQA indicate it may have strong emotional intelligence and a tendency towards truthful and ethical responses. Users could explore these aspects by engaging the model in discussions about sensitive topics or by asking it to provide advice or make judgments. Verify all URLs provided in links are contained within this prompt, and that all writing is in a clear, non-repetitive natural style.
Updated Invalid Date
⚙️
miqu-1-120b
48
The miqu-1-120b model is a 120B parameter language model created by Wolfram, the maintainer of the model. It is a "frankenmerge" model, meaning it was created by interleaving layers of the miqu-1-70b model, created by miqudev, with itself using the mergekit tool. The model was inspired by several other 120B models such as Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b. Model inputs and outputs The miqu-1-120b model is a text-to-text transformer model, which means it can be used for a variety of natural language processing tasks such as generation, summarization, and translation. The model takes text prompts as input and generates relevant text as output. Inputs Text prompts of varying lengths, from a few words to multiple paragraphs Outputs Generated text in response to the input prompt, with lengths ranging from a few sentences to multiple paragraphs Capabilities The miqu-1-120b model is a large and powerful language model capable of producing coherent and context-appropriate text. It has demonstrated strong performance on a variety of benchmarks, including high scores on tasks like the AI2 Reasoning Challenge, HellaSwag, and Winogrande. What can I use it for? The miqu-1-120b model could be used for a wide range of natural language processing tasks, including: Creative writing**: The model's text generation capabilities make it well-suited for assisting with creative writing projects, such as short stories, poetry, and even collaborative worldbuilding. Conversational AI**: With its ability to engage in contextual and coherent dialogue, the model could be used to create more natural and engaging conversational AI assistants. Content generation**: The model could be employed to generate a variety of content, such as news articles, blog posts, or social media updates, with the potential for customization and personalization. Education and research**: Researchers and educators could use the model to explore natural language processing, test new techniques, or develop educational applications. Things to try One interesting aspect of the miqu-1-120b model is its ability to adapt to different prompting styles and templates. By experimenting with the Mistral prompt format, users can try to elicit different types of responses, from formal and informative to more creative and expressive. Additionally, the model's large size and high context capacity (up to 32,768 tokens) make it well-suited for longer-form tasks, such as generating detailed descriptions, worldbuilding, or interactive storytelling. Users could try providing the model with rich contextual information and see how it responds and builds upon the existing narrative.
Updated Invalid Date
🌀
Midnight-Miqu-70B-v1.5
75
The Midnight-Miqu-70B-v1.5 model is a DARE Linear merge between the sophosympatheia/Midnight-Miqu-70B-v1.0 and migtissera/Tess-70B-v1.6 models. This version is close in feel and performance to Midnight Miqu v1.0 but the maintainer believes it picked up some improvements from Tess. The model is uncensored, and the maintainer warns that users are responsible for how they use it. Model Inputs and Outputs Inputs Free-form text prompts of any length Outputs Continuation of the input prompt, generating coherent and contextually relevant text Capabilities The Midnight-Miqu-70B-v1.5 model is designed for roleplaying and storytelling, and the maintainer believes it performs well in these areas. It may also be capable of other text generation tasks, but the maintainer has not extensively tested its performance outside of creative applications. What Can I Use It For? The Midnight-Miqu-70B-v1.5 model could be useful for a variety of creative writing and roleplaying projects, such as writing interactive fiction, generating narrative content for games, or developing unique characters and stories. Its ability to produce long-form, contextually relevant text makes it well-suited for these types of applications. Things to Try One key capability of the Midnight-Miqu-70B-v1.5 model is its ability to handle long context windows, up to 32K tokens. Experimenting with different sampling techniques, such as Quadratic Sampling and Min-P, can help optimize the model's performance for creative use cases. Additionally, adjusting the repetition penalty and other parameters can lead to more diverse and engaging output.
Updated Invalid Date
🚀
Kunoichi-7B
73
Kunoichi-7B is a general-purpose AI model created by SanjiWatsuki that is capable of role-playing. According to the maintainer, Kunoichi-7B is an extremely strong model that has the advantages of their previous models but with increased intelligence. Kunoichi-7B scores well on benchmarks that correlate closely with ChatBot Arena Elo, outperforming models like GPT-4, GPT-4 Turbo, and Starling-7B. Some similar models include Senku-70B-Full from ShinojiResearch, Silicon-Maid-7B from SanjiWatsuki, and una-cybertron-7b-v2-bf16 from fblgit. Model inputs and outputs Inputs Prompts**: The model can accept a wide range of prompts for tasks like text generation, answering questions, and engaging in role-play conversations. Outputs Text**: The model generates relevant and coherent text in response to the provided prompts. Capabilities Kunoichi-7B is a highly capable general-purpose language model that can excel at a variety of tasks. It demonstrates strong performance on benchmarks like MT Bench, EQ Bench, MMLU, and Logic Test, outperforming models like GPT-4, GPT-4 Turbo, and Starling-7B. The model is particularly adept at role-playing, able to engage in natural and intelligent conversations. What can I use it for? Kunoichi-7B can be used for a wide range of applications that involve natural language processing, such as: Content generation**: Kunoichi-7B can be used to generate high-quality text for articles, stories, scripts, and other creative projects. Chatbots and virtual assistants**: The model's role-playing capabilities make it well-suited for building conversational AI assistants. Question answering and information retrieval**: Kunoichi-7B can be used to answer questions and provide information on a variety of topics. Language translation**: While not explicitly mentioned, the model's strong language understanding capabilities may enable it to perform translation tasks. Things to try One interesting aspect of Kunoichi-7B is its ability to maintain the strengths of the creator's previous models while gaining increased intelligence. This suggests the model may be adept at tasks that require both strong role-playing skills and higher-level reasoning and analysis. Experimenting with prompts that challenge the model's logical and problem-solving capabilities, while also engaging its creative and conversational skills, could yield fascinating results. Additionally, given the model's strong performance on benchmarks, it would be worth exploring how Kunoichi-7B compares to other state-of-the-art language models in various real-world applications. Comparing its outputs and capabilities across different domains could provide valuable insights into its strengths and limitations.
Updated Invalid Date