Tomasmcm

Models by this creator

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llamaguard-7b

tomasmcm

Total Score

339

llamaguard-7b is a 7 billion parameter Llama 2-based model developed by tomasmcm that serves as an input-output safeguard. It is similar in size and capabilities to models like codellama-7b, codellama-7b-instruct, and other Llama-based models. The key focus of llamaguard-7b is on providing input-output safeguards to help ensure safe and responsible language model usage. Model inputs and outputs llamaguard-7b takes a text prompt as input and generates a text output. The model provides various input parameters to control the generation, including: Inputs Prompt**: The text prompt to send to the model. Max Tokens**: The maximum number of tokens to generate per output sequence. Temperature**: A float that controls the randomness of the sampling, with lower values making the model more deterministic and higher values making it more random. Presence Penalty**: A float that penalizes new tokens based on whether they appear in the generated text so far, encouraging the use of new tokens. Frequency Penalty**: A float that penalizes new tokens based on their frequency in the generated text so far, also encouraging the use of new tokens. Top K**: An integer that controls the number of top tokens to consider, with -1 meaning to consider all tokens. Top P**: A float that controls the cumulative probability of the top tokens to consider, with values between 0 and 1. Stop**: A list of strings that, if generated, will stop the generation. Outputs Output**: The generated text output from the model. Capabilities llamaguard-7b is a capable language model that can be used for a variety of natural language processing tasks, such as text generation, summarization, and translation. It is particularly well-suited for applications that require safe and responsible language model usage, as it includes various safeguards and controls to help ensure the generated output is appropriate and aligned with the user's intentions. What can I use it for? llamaguard-7b can be used in a wide range of applications, including content creation, chatbots, virtual assistants, and even creative writing. Its input-output safeguards make it well-suited for use cases where the generated text needs to be carefully controlled, such as in educational, medical, or financial contexts. Additionally, the model's flexibility allows it to be fine-tuned or adapted for more specialized use cases, such as code generation or programming assistance. Things to try One interesting aspect of llamaguard-7b is its ability to generate text with a high degree of coherence and logical consistency, while still maintaining a sense of creativity and originality. Users can experiment with adjusting the model's input parameters, such as temperature and top-k/top-p values, to explore the range of outputs and find the sweet spot that works best for their specific use case. Additionally, users can try providing the model with different types of prompts, from creative writing exercises to task-oriented instructions, to see how it responds and adapts to different contexts.

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Updated 6/21/2024

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zephyr-7b-beta

tomasmcm

Total Score

185

zephyr-7b-beta is the second model in the Zephyr series of language models developed by tomasmcm, aimed at serving as helpful AI assistants. It is a 7 billion parameter model that builds upon the capabilities of its predecessor, the original Zephyr model. Like the mistral-7b-v0.1 and prometheus-13b-v1.0 models, zephyr-7b-beta is designed as an alternative to GPT-4 for evaluating large language models and reward models for reinforcement learning from human feedback (RLHF). Model inputs and outputs The zephyr-7b-beta model takes a text prompt as input and generates a text output. The prompt can include instructions, questions, or open-ended text, and the model will attempt to produce a relevant and coherent response. The output is generated using techniques like top-k and top-p filtering, with configurable parameters to control the diversity and creativity of the generated text. Inputs prompt**: The text prompt to send to the model. max_new_tokens**: The maximum number of new tokens the model should generate as output. temperature**: The value used to modulate the next token probabilities. top_p**: A probability threshold for generating the output, using nucleus filtering. top_k**: The number of highest probability tokens to consider for generating the output. presence_penalty**: A penalty applied to tokens that have already appeared in the output. Outputs output**: The text generated by the model in response to the input prompt. Capabilities zephyr-7b-beta is capable of engaging in open-ended conversations, answering questions, and generating text on a wide range of topics. It has been trained to be helpful and informative, and can assist with tasks like brainstorming, research, and analysis. The model's capabilities are similar to those of the yi-6b-chat and qwen1.5-72b models, though the exact performance may vary. What can I use it for? zephyr-7b-beta can be used for a variety of applications, such as building chatbots, virtual assistants, and content generation tools. It could be used to help with tasks like writing, research, and analysis, or to engage in open-ended conversations on a wide range of topics. The model's capabilities make it a useful tool for both personal and professional use, and its flexible input and output options allow it to be integrated into a variety of applications. Things to try One interesting aspect of zephyr-7b-beta is its potential for use in evaluating other large language models and reward models for RLHF, as mentioned earlier. By comparing the model's performance on tasks like question answering or text generation to that of other models, researchers and developers can gain insights into the strengths and weaknesses of different approaches to language modeling and alignment. Additionally, the model's flexibility and general-purpose nature make it a valuable tool for experimentation and exploration in the field of AI and natural language processing.

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Updated 6/21/2024

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llama2-13b-tiefighter

tomasmcm

Total Score

166

llama2-13b-tiefighter is a large language model developed by the KoboldAI community. It is a merged model created by combining multiple pre-existing models, including LLaMA2-13B-Tiefighter and LLaMA2-13B-Tiefighter-GGUF. The model aims to be a creative and versatile tool, capable of tasks like story writing, persona-based chatbots, and interactive adventures. Model inputs and outputs Inputs prompt**: The text prompt to send to the model. max_tokens**: The maximum number of tokens to generate per output sequence. temperature**: A float that controls the randomness of the sampling, with lower values making the model more deterministic and higher values making it more random. top_k**: An integer that controls the number of top tokens to consider during generation. top_p**: A float that controls the cumulative probability of the top tokens to consider, must be between 0 and 1. presence_penalty**: A float that penalizes new tokens based on whether they appear in the generated text so far. frequency_penalty**: A float that penalizes new tokens based on their frequency in the generated text so far. stop**: A list of strings that will stop the generation when they are generated. Outputs Output**: The generated text output from the model. Capabilities llama2-13b-tiefighter is a highly capable model that can engage in a variety of creative tasks. It excels at story writing, allowing users to either continue an existing story or generate a new one from scratch. The model is also well-suited for persona-based chatbots, where it can improvise believable conversations based on simple prompts or character descriptions. What can I use it for? This model is an excellent choice for anyone looking to explore the creative potential of large language models. Writers, roleplayers, and interactive fiction enthusiasts can all find use for llama2-13b-tiefighter. The model's ability to generate coherent and engaging narratives makes it a valuable tool for world-building, character development, and open-ended storytelling. Things to try One interesting aspect of llama2-13b-tiefighter is its integration of adventure game-style mechanics. By using the > prefix for user commands, you can engage the model in interactive adventure scenarios, where it will respond based on its training on choose-your-own-adventure datasets. Experimenting with different prompts and command styles can lead to unique and unpredictable narrative experiences.

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Updated 5/16/2024

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starling-lm-7b-alpha

tomasmcm

Total Score

38

The starling-lm-7b-alpha is an open large language model (LLM) developed by berkeley-nest and trained using Reinforcement Learning from AI Feedback (RLAIF). The model is built upon the Openchat 3.5 base model and uses the berkeley-nest/Starling-RM-7B-alpha reward model and the advantage-induced policy alignment (APA) policy optimization method. The starling-lm-7b-alpha model scores 8.09 on the MT Bench benchmark, outperforming many other LLMs except for OpenAI's GPT-4 and GPT-4 Turbo. Similar models include the Starling-LM-7B-beta which uses an upgraded reward model and policy optimization technique, as well as stable-diffusion and stablelm-tuned-alpha-7b from Stability AI. Model inputs and outputs Inputs prompt**: The text prompt to send to the model. max_tokens**: The maximum number of tokens to generate per output sequence. temperature**: A float that controls the randomness of the sampling, with lower values making the model more deterministic and higher values making it more random. top_k**: An integer that controls the number of top tokens to consider during generation. top_p**: A float that controls the cumulative probability of the top tokens to consider, with values between 0 and 1. presence_penalty**: A float that penalizes new tokens based on whether they appear in the generated text so far, with values greater than 0 encouraging the use of new tokens and values less than 0 encouraging token repetition. frequency_penalty**: A float that penalizes new tokens based on their frequency in the generated text so far, with values greater than 0 encouraging the use of new tokens and values less than 0 encouraging token repetition. stop**: A list of strings that, when generated, will stop the generation process. Outputs Output**: A string containing the generated text. Capabilities The starling-lm-7b-alpha model is capable of generating high-quality text on a wide range of topics, outperforming many other LLMs on benchmark tasks. It can be used for tasks such as language translation, question answering, and creative writing, among others. What can I use it for? The starling-lm-7b-alpha model can be used for a variety of natural language processing tasks, such as: Content Generation**: The model can be used to generate high-quality text for articles, stories, or other types of content. Language Translation**: The model can be fine-tuned for language translation tasks, allowing it to translate text between different languages. Question Answering**: The model can be used to answer a wide range of questions on various topics. Chatbots and Conversational AI**: The model can be used to build conversational AI applications, such as virtual assistants or chatbots. The model is hosted on the LMSYS Chatbot Arena platform, allowing users to test and experiment with the model for free. Things to try One interesting aspect of the starling-lm-7b-alpha model is its ability to generate text with a high degree of coherence and consistency. By adjusting the temperature and other generation parameters, users can experiment with the model's creativity and expressiveness, while still maintaining a clear and logical narrative flow. Additionally, the model's strong performance on benchmark tasks suggests it could be a valuable tool for a wide range of natural language processing applications. Users may want to explore fine-tuning the model for specific domains or tasks, or integrating it into larger AI systems to leverage its capabilities.

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Updated 6/21/2024

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prometheus-13b-v1.0

tomasmcm

Total Score

31

The prometheus-13b-v1.0 is an alternative to GPT-4 when evaluating large language models (LLMs) and reward models for reinforcement learning from human feedback (RLHF). It was developed by tomasmcm, the same creator behind the llamaguard-7b and qwen1.5-72b models. Similar to the codellama-13b and llava-13b models, the prometheus-13b-v1.0 is a 13 billion parameter model focused on specific capabilities. Model inputs and outputs The prometheus-13b-v1.0 model takes in a text prompt and generates output text. The input and output specifications are as follows: Inputs Prompt**: The text prompt to send to the model. Max Tokens**: The maximum number of tokens to generate per output sequence. Temperature**: A float that controls the randomness of the sampling, with lower values making the model more deterministic and higher values making it more random. Presence Penalty**: A float that penalizes new tokens based on whether they appear in the generated text so far, with values > 0 encouraging the use of new tokens and values 0 encouraging the use of new tokens and values < 0 encouraging the repetition of tokens. Top K**: An integer that controls the number of top tokens to consider, with -1 meaning to consider all tokens. Top P**: A float that controls the cumulative probability of the top tokens to consider, with values between 0 and 1. Stop**: A list of strings that stop the generation when they are generated. Outputs Output**: The generated text output. Capabilities The prometheus-13b-v1.0 model is capable of generating high-quality text that can be used for a variety of tasks, such as content creation, question answering, and language modeling. It is particularly useful for evaluating the performance of other LLMs and reward models for RLHF. What can I use it for? The prometheus-13b-v1.0 model can be used for a variety of applications, such as: Content creation: The model can be used to generate text for blog posts, articles, and other types of content. Language modeling: The model can be used to evaluate the performance of other LLMs by comparing their outputs to the prometheus-13b-v1.0 model's outputs. Reward modeling: The model can be used to evaluate the performance of reward models for RLHF by comparing their outputs to the prometheus-13b-v1.0 model's outputs. Things to try Some interesting things to try with the prometheus-13b-v1.0 model include: Experimenting with different parameter settings, such as temperature and top-k/top-p, to see how they affect the model's output. Comparing the model's outputs to those of other LLMs to evaluate its performance. Using the model as a baseline for evaluating the performance of reward models for RLHF. Exploring the model's capabilities in specific domains, such as question answering or content generation.

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Updated 6/21/2024

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solar-10.7b-instruct-v1.0

tomasmcm

Total Score

3

The solar-10.7b-instruct-v1.0 model is a powerful language model developed by tomasmcm. It is part of the SOLAR family of models, which aim to elevate the performance of language models through Upstage Depth UP Scaling. The solar-10.7b-instruct-v1.0 model is an instructionally-tuned variant of the SOLAR 10.7B base model, providing enhanced capabilities for following and executing instructions. This model shares similarities with other instruction-tuned models like Nous Hermes 2 - SOLAR 10.7B, Mistral-7B-Instruct-v0.1, and Mistral-7B-Instruct-v0.2, all of which aim to provide improved instruction-following capabilities compared to their base models. Model inputs and outputs The solar-10.7b-instruct-v1.0 model takes a text prompt as input and generates a text output. The key input parameters include: Inputs Prompt**: The text prompt to send to the model. Max Tokens**: The maximum number of tokens to generate per output sequence. Temperature**: A float that controls the randomness of the sampling, with lower values making the model more deterministic and higher values making it more random. Presence Penalty**: A float that penalizes new tokens based on whether they appear in the generated text so far, encouraging the use of new tokens. Frequency Penalty**: A float that penalizes new tokens based on their frequency in the generated text so far, also encouraging the use of new tokens. Top K**: An integer that controls the number of top tokens to consider, with -1 meaning to consider all tokens. Top P**: A float that controls the cumulative probability of the top tokens to consider, with values between 0 and 1. Stop**: A list of strings that stop the generation when they are generated. Outputs The model outputs a single string of text. Capabilities The solar-10.7b-instruct-v1.0 model is capable of understanding and executing a wide variety of instructions, from creative writing tasks to analysis and problem-solving. It can generate coherent and contextually-appropriate text, demonstrating strong language understanding and generation abilities. What can I use it for? The solar-10.7b-instruct-v1.0 model can be used for a wide range of natural language processing tasks, such as: Content creation (e.g., articles, stories, scripts) Question answering and information retrieval Summarization and text simplification Code generation and programming assistance Dialogue and chatbot systems Personalized recommendations and decision support As with any powerful language model, it's important to use the solar-10.7b-instruct-v1.0 model responsibly and ensure that its outputs are aligned with your intended use case. Things to try One interesting aspect of the solar-10.7b-instruct-v1.0 model is its ability to follow complex instructions and generate detailed, coherent responses. For example, you could try providing it with a set of instructions for a creative writing task, such as "Write a short story about a time traveler who gets stranded in the past. Incorporate elements of mystery, adventure, and personal growth." The model should be able to generate a compelling narrative that adheres to the provided instructions. Another interesting experiment would be to explore the model's capabilities in the realm of analysis and problem-solving. You could try giving it a complex question or task, such as "Analyze the economic impact of a proposed policy change in the healthcare sector, considering factors such as cost, access, and patient outcomes." The model should be able to provide a thoughtful and well-reasoned response, drawing on its extensive knowledge base.

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Updated 6/21/2024

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claude2-alpaca-13b

tomasmcm

Total Score

2

claude2-alpaca-13b is a large language model developed by Replicate and the UMD-Zhou-Lab. It is a fine-tuned version of Meta's Llama-2 model, using the Claude2 Alpaca dataset. This model shares similarities with other Llama-based models like llama-2-7b-chat, codellama-34b-instruct, and codellama-13b, which are also designed for tasks like coding, conversation, and instruction-following. However, claude2-alpaca-13b is uniquely trained on the Claude2 Alpaca dataset, which may give it distinct capabilities compared to these other models. Model inputs and outputs claude2-alpaca-13b is a text-to-text generation model, taking in a text prompt as input and generating relevant text as output. The model supports configurable parameters like top_k, top_p, temperature, presence_penalty, and frequency_penalty to control the sampling process and the diversity of the generated output. Inputs Prompt**: The text prompt to send to the model. Max Tokens**: The maximum number of tokens to generate per output sequence. Temperature**: A float that controls the randomness of the sampling, with lower values making the model more deterministic and higher values making it more random. Presence Penalty**: A float that penalizes new tokens based on whether they appear in the generated text so far, encouraging the model to use new tokens. Frequency Penalty**: A float that penalizes new tokens based on their frequency in the generated text so far, also encouraging the model to use new tokens. Outputs Output**: The text generated by the model in response to the input prompt. Capabilities The claude2-alpaca-13b model is capable of generating coherent and relevant text across a wide range of domains, from creative writing to task-oriented instructions. Its training on the Claude2 Alpaca dataset may give it particular strengths in areas like conversation, open-ended problem-solving, and task-completion. What can I use it for? The versatile capabilities of claude2-alpaca-13b make it suitable for a variety of applications, such as: Content Generation**: Producing engaging and informative text for blogs, articles, or social media posts. Conversational AI**: Building chatbots and virtual assistants that can engage in natural, human-like dialogue. Task-oriented Assistants**: Developing applications that can help users with various tasks, from research to analysis to creative projects. The model's large size and specialized training data mean it may be particularly well-suited for monetization by companies looking to integrate advanced language AI into their products or services. Things to try Some interesting things to explore with claude2-alpaca-13b include: Prompting the model with open-ended questions or scenarios to see how it responds creatively. Experimenting with the model's configuration parameters to generate more or less diverse, deterministic, or novel output. Comparing the model's performance to other Llama-based models like llama-2-13b-chat and codellama-13b-instruct to understand its unique strengths and weaknesses. By pushing the boundaries of what claude2-alpaca-13b can do, you can uncover new and exciting applications for this powerful language model.

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Updated 6/21/2024