Teknium
Models by this creator
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OpenHermes-2.5-Mistral-7B
780
OpenHermes-2.5-Mistral-7B is a state-of-the-art large language model (LLM) developed by teknium. It is a continuation of the OpenHermes 2 model, which was trained on additional code datasets. This fine-tuning on code data has boosted the model's performance on several non-code benchmarks, including TruthfulQA, AGIEval, and the GPT4All suite, though it did reduce the score on BigBench. Compared to the previous OpenHermes 2 model, the OpenHermes-2.5-Mistral-7B has improved its Humaneval score from 43% to 50.7% at Pass 1. It was trained on 1 million entries of primarily GPT-4 generated data, as well as other high-quality datasets from across the AI landscape. The model is similar to other Mistral-based models like Mistral-7B-Instruct-v0.2 and Mixtral-8x7B-v0.1, sharing architectural choices such as Grouped-Query Attention, Sliding-Window Attention, and a Byte-fallback BPE tokenizer. Model inputs and outputs Inputs Text prompts**: The model accepts natural language text prompts as input, which can include requests for information, instructions, or open-ended conversation. Outputs Generated text**: The model outputs generated text that responds to the input prompt. This can include answers to questions, task completion, or open-ended dialogue. Capabilities The OpenHermes-2.5-Mistral-7B model has demonstrated strong performance across a variety of benchmarks, including improvements in code-related tasks. It can engage in substantive conversations on a wide range of topics, providing detailed and coherent responses. The model also exhibits creativity and can generate original ideas and solutions. What can I use it for? With its broad capabilities, OpenHermes-2.5-Mistral-7B can be used for a variety of applications, such as: Conversational AI**: Develop intelligent chatbots and virtual assistants that can engage in natural language interactions. Content generation**: Create original text content, such as articles, stories, or scripts, to support content creation and publishing workflows. Code generation and optimization**: Leverage the model's code-related capabilities to assist with software development tasks, such as generating code snippets or refactoring existing code. Research and analysis**: Utilize the model's language understanding and reasoning abilities to support tasks like question answering, summarization, and textual analysis. Things to try One interesting aspect of the OpenHermes-2.5-Mistral-7B model is its ability to converse on a wide range of topics, from programming to philosophy. Try exploring the model's conversational capabilities by engaging it in discussions on diverse subjects, or by tasking it with creative writing exercises. The model's strong performance on code-related benchmarks also suggests it could be a valuable tool for software development workflows, so experimenting with code generation and optimization tasks could be a fruitful avenue to explore.
Updated 5/28/2024
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OpenHermes-2-Mistral-7B
254
The OpenHermes-2-Mistral-7B is a state-of-the-art language model developed by teknium. It is an advanced version of the previous OpenHermes models, trained on a larger and more diverse dataset of over 900,000 entries. The model has been fine-tuned on the Mistral architecture, giving it enhanced capabilities in areas like natural language understanding and generation. The model is compared to similar offerings like the OpenHermes-2.5-Mistral-7B, Hermes-2-Pro-Mistral-7B, and NeuralHermes-2.5-Mistral-7B. While they share a common lineage, each model has its own unique strengths and capabilities. Model inputs and outputs The OpenHermes-2-Mistral-7B is a text-to-text model, capable of accepting a wide range of natural language inputs and generating relevant and coherent responses. Inputs Natural language prompts**: The model can accept freeform text prompts on a variety of topics, from general conversation to specific tasks and queries. System prompts**: The model also supports more structured system prompts that can provide context and guidance for the desired output. Outputs Natural language responses**: The model generates relevant and coherent text responses to the provided input, demonstrating strong natural language understanding and generation capabilities. Structured outputs**: In addition to open-ended text, the model can also produce structured outputs like JSON objects, which can be useful for certain applications. Capabilities The OpenHermes-2-Mistral-7B model showcases impressive performance across a range of benchmarks and evaluations. On the GPT4All benchmark, it achieves an average score of 73.12, outperforming both the OpenHermes-1 Llama-2 13B and OpenHermes-2 Mistral 7B models. The model also excels on the AGIEval benchmark, scoring 43.07% on average, a significant improvement over the earlier OpenHermes-1 and OpenHermes-2 versions. Its performance on the BigBench Reasoning Test, with an average score of 40.96%, is also noteworthy. In terms of specific capabilities, the model demonstrates strong text generation abilities, handling tasks like creative writing, analytical responses, and open-ended conversation with ease. Its structured outputs, particularly in the form of JSON objects, also make it a useful tool for applications that require more formal, machine-readable responses. What can I use it for? The OpenHermes-2-Mistral-7B model can be a valuable asset for a wide range of applications and use cases. Some potential areas of use include: Content creation**: The model's strong text generation capabilities make it useful for tasks like article writing, blog post generation, and creative storytelling. Intelligent assistants**: The model's natural language understanding and generation abilities make it well-suited for building conversational AI assistants to help users with a variety of tasks. Data analysis and visualization**: The model's ability to produce structured JSON outputs can be leveraged for data processing, analysis, and visualization applications. Educational and research applications**: The model's broad knowledge base and analytical capabilities make it a useful tool for educational purposes, such as question-answering, tutoring, and research support. Things to try One interesting aspect of the OpenHermes-2-Mistral-7B model is its ability to engage in multi-turn dialogues and leverage system prompts to guide the conversation. By using the model's ChatML-based prompt format, users can establish specific roles, rules, and stylistic choices for the model to adhere to, opening up new and creative ways to interact with the AI. Additionally, the model's structured output capabilities, particularly in the form of JSON objects, present opportunities for building applications that require more formal, machine-readable responses. Developers can explore ways to integrate the model's JSON generation into their workflows, potentially automating certain data-driven tasks or enhancing the intelligence of their applications.
Updated 5/28/2024
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Mistral-Trismegistus-7B
195
The Mistral-Trismegistus-7B model is a unique AI assistant created by teknium that specializes in tasks related to the esoteric, occult, and spiritual. Unlike many AI models focused on positivity, this model embraces the full depth and richness of the esoteric world. It was trained on a synthetic dataset of around 10,000 high-quality examples covering a wide range of occult and spiritual knowledge and tasks. This makes it particularly well-suited for answering questions about occult artifacts, playing the role of a hypnotist, and engaging in other mystical and esoteric activities. Similar models include the OpenHermes-2-Mistral-7B and OpenHermes-2.5-Mistral-7B models, also created by teknium. These models share a similar focus on the esoteric and spiritual, but with some differences in their training and capabilities. Model inputs and outputs The Mistral-Trismegistus-7B model is a text-to-text model, meaning it takes text prompts as input and generates text outputs in response. The model is particularly adept at understanding and engaging with prompts related to the occult, esoteric, and spiritual domains. Inputs Occult and esoteric prompts**: The model can handle a wide range of prompts related to the occult, esoteric, and spiritual, such as questions about occult artifacts or requests to play the role of a hypnotist. General language prompts**: While the model is specialized in esoteric tasks, it can also handle more general language prompts, such as open-ended questions or creative writing exercises. Outputs Detailed and nuanced responses**: The model generates detailed, rich, and thoughtful responses to prompts, drawing on its extensive training in occult and esoteric knowledge. Creative and imaginative outputs**: The model is not constrained by a "positivity-first" approach, allowing it to explore the full depth and complexity of the esoteric world in its outputs. Capabilities The Mistral-Trismegistus-7B model excels at tasks related to the occult, esoteric, and spiritual domains. It can provide detailed and nuanced information about occult artifacts, engage in role-playing as a hypnotist, and generally explore the mysteries of the esoteric world. The model's unique training approach allows it to delve into these topics without the limitations of a "positivity-first" mindset that can sometimes constrain other AI models. What can I use it for? The Mistral-Trismegistus-7B model is well-suited for a variety of applications related to the occult, esoteric, and spiritual. Some potential use cases include: Occult education and research**: The model can be used to answer questions, provide information, and engage in discussion about occult artifacts, rituals, and practices. Esoteric role-playing and creative writing**: The model can be used to facilitate immersive, imaginative experiences in the realm of the esoteric, such as roleplaying as a hypnotist or exploring metaphysical concepts. Spiritual exploration and self-discovery**: The model's depth of knowledge and openness to the complexities of the spiritual world can be leveraged to support personal growth, meditation, and introspection. Things to try One interesting aspect of the Mistral-Trismegistus-7B model is its willingness to explore the full depth and nuance of the esoteric world, rather than constraining itself to a narrow "positivity-first" approach. This allows for a more authentic and multifaceted engagement with the mysteries of the occult and spiritual realms. For example, you could prompt the model to delve into the symbolism and significance of a particular occult artifact, or to engage in a thought-provoking discussion about the nature of consciousness and the human experience. The model's responses are likely to be rich, insightful, and unencumbered by artificial limitations, providing a unique and captivating exploration of the esoteric. Another interesting avenue to explore with this model is the use of esoteric role-playing and creative writing. By prompting the model to adopt different personas or narrative perspectives, you can unlock a world of imaginative possibilities and uncover new layers of understanding about the occult and spiritual realms.
Updated 5/28/2024
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CollectiveCognition-v1.1-Mistral-7B
77
The CollectiveCognition-v1.1-Mistral-7B model is a state-of-the-art language model developed by teknium. It is a fine-tuned version of the Mistral approach, which is notable for its exceptional performance on the TruthfulQA benchmark. This benchmark assesses models for common misconceptions, potentially indicating hallucination rates. The model was trained on a limited dataset of only 100 data points gathered from a platform reminiscent of ShareGPT, yet it is able to compete with much larger 70B models on this important metric. Similar models include the OpenHermes-2.5-Mistral-7B and the SynthIA-7B-v1.3, both of which also leverage the Mistral approach and have demonstrated strong performance on a variety of benchmarks. Model inputs and outputs The CollectiveCognition-v1.1-Mistral-7B model is a text-to-text AI assistant, meaning it takes text prompts as input and generates text outputs in response. Inputs Prompts**: The model accepts natural language prompts from users, which can cover a wide range of topics and tasks. Outputs Generated text**: The model produces coherent, contextually relevant text in response to the input prompts. This can include answers to questions, explanations of concepts, creative writing, and more. Capabilities The CollectiveCognition-v1.1-Mistral-7B model is particularly notable for its strong performance on the TruthfulQA benchmark, which assesses a model's ability to avoid common misconceptions and hallucinations. This suggests the model has a robust understanding of facts and reasoning, making it well-suited for tasks that require truthful, reliable information. What can I use it for? The CollectiveCognition-v1.1-Mistral-7B model could be useful for a variety of applications that require a language model with high accuracy and truthfulness, such as: Question-answering systems**: The model's strong performance on TruthfulQA indicates it could be a valuable component in building AI-powered Q&A services. Content creation assistance**: The model could help writers, researchers, and others generate high-quality, truthful content more efficiently. Chatbots and virtual assistants**: The model's capabilities could be leveraged to build conversational AI systems that provide reliable, trustworthy information. Things to try One interesting aspect of the CollectiveCognition-v1.1-Mistral-7B model is its ability to perform well on a benchmark like TruthfulQA despite being trained on a relatively small dataset. This suggests the model may have strong generalization abilities, which could be explored further by testing its performance on a wider range of tasks and datasets. Additionally, given the model's focus on truthfulness and accuracy, it would be worth investigating how it handles tasks that require nuanced reasoning or the ability to navigate complex, ambiguous information. Prompts that challenge the model's understanding of context and subtlety could yield valuable insights into its capabilities and limitations.
Updated 5/28/2024
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Replit-v2-CodeInstruct-3B
73
The Replit-v2-CodeInstruct-3B model is a 3 billion parameter AI model developed by teknium that has been fine-tuned on both the CodeAlpaca and GPTeacher Code-Instruct datasets to give it code instruction capabilities. This model builds upon the replit-code-v1-3b base model, which was trained on a diverse set of programming languages. The fine-tuning process has given the Replit-v2-CodeInstruct-3B model the ability to follow code-related instructions and generate relevant responses. Model Inputs and Outputs Inputs Code-related prompts and instructions**: The model is designed to accept text-based prompts and instructions related to coding tasks, such as "Write a function that computes the Fibonacci sequence up to n" or "Explain how this code snippet works." Outputs Generated code and text responses**: The model can generate relevant code snippets and text-based responses to address the provided instructions and prompts. The outputs aim to be helpful, informative, and aligned with the user's intent. Capabilities The Replit-v2-CodeInstruct-3B model is capable of engaging in a wide range of code-related tasks, such as code completion, code explanation, and generating code based on natural language instructions. It can handle prompts across multiple programming languages, including Python, JavaScript, Java, and more. The model's fine-tuning on the CodeAlpaca and GPTeacher datasets has improved its ability to follow instructions and provide helpful, coherent responses. What Can I Use It For? The Replit-v2-CodeInstruct-3B model can be a valuable tool for developers and researchers working on projects that involve code generation, code understanding, and code-related task completion. It can be used to build applications that assist programmers by providing code suggestions, explanations, and solutions to coding problems. Additionally, the model could be further fine-tuned or integrated into educational resources or coding learning tools to support students and beginners in their programming journeys. Things to Try One interesting thing to try with the Replit-v2-CodeInstruct-3B model is to explore its ability to handle code-related prompts that involve multiple steps or complex instructions. For example, you could try asking the model to write a function that solves a specific coding challenge, or to explain the inner workings of a given code snippet in detail. Experimenting with different types of prompts and observing the model's responses can help you better understand its capabilities and limitations.
Updated 5/27/2024
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OpenHermes-13B
53
OpenHermes-13B is a large language model (LLM) developed by teknium that has been fine-tuned on over 242,000 entries of primarily GPT-4 generated data. This includes datasets from sources like GPTeacher, WizardLM, Airoboros GPT-4, Camel-AI, CodeAlpaca, and more. The model was trained to excel at a variety of language tasks, from text generation to following complex instructions. One key difference between OpenHermes-13B and similar models like OpenHermes-2.5-Mistral-7B is the fully open-source nature of its training dataset. This allows for greater transparency and opportunities for further research and development. Model inputs and outputs Inputs Natural language prompts and instructions covering a wide range of topics and tasks Outputs Coherent, context-aware responses in natural language Completion of complex tasks and instructions Generation of creative and informative text Capabilities OpenHermes-13B demonstrates impressive capabilities across a variety of benchmarks, including strong performance on the GPT4All, AGIEval, and BigBench test suites. The model is particularly adept at following instructions, understanding context, and generating high-quality text. What can I use it for? With its broad knowledge and flexible language understanding, OpenHermes-13B can be useful for a wide range of applications, such as: Chatbots and virtual assistants Content generation (e.g., articles, stories, scripts) Task completion and instruction following Question answering and knowledge retrieval Educational and research applications Things to try One interesting aspect of OpenHermes-13B is its ability to engage in multi-turn dialogues and roleplay scenarios, as demonstrated by the example outputs. This could be an area to further explore, such as by creating interactive chatbots or virtual characters. Additionally, the model's strong performance on benchmarks related to reasoning, logical deduction, and understanding of complex concepts suggests potential applications in fields like education, scientific research, and problem-solving.
Updated 5/28/2024