llama2-13b-tiefighter
Maintainer: tomasmcm
166
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | No Github link provided |
| Paper link | View on Arxiv |
Create account to get full access
Model overview
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.
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
🗣️
LLaMA2-13B-Tiefighter
71
LLaMA2-13B-Tiefighter is a large language model developed by KoboldAI. It is a merged model created by combining multiple fine-tuned models on top of the base Undi95/Xwin-MLewd-13B-V0.2 model. The model incorporates enhancements from various upstream models, including PocketDoc/Dans-RetroRodeo-13b and Blackroot/Llama-2-13B-Storywriter-LORA, to create a versatile text generation model with a focus on creativity and storytelling. Model inputs and outputs Inputs Text**: The model takes in text as input, which can be used to prompt the model to generate additional text. Outputs Text**: The model generates text as output, with a focus on creative and narrative-driven content. Capabilities The LLaMA2-13B-Tiefighter model is designed to be a flexible and creative text generation tool. By combining multiple fine-tuned models, the developers have created a model that excels at tasks like story writing, world-building, and open-ended conversational interactions. The model's diverse training data and merging process allow it to generate coherent and engaging text across a variety of genres and styles. What can I use it for? The LLaMA2-13B-Tiefighter model can be a valuable tool for writers, game developers, and creative professionals who need to generate original text content. Its capabilities in narrative-driven tasks make it suitable for applications like interactive fiction, roleplaying games, and AI-assisted worldbuilding. While the model is not intended for safety-critical applications, it can be a powerful asset for projects that prioritize creativity and imagination. Things to try One interesting aspect of the LLaMA2-13B-Tiefighter model is its ability to improvise and generate unexpected yet coherent text when given minimal prompts. By allowing the model to freely generate content without constraining it with detailed instructions, users can discover novel ideas and narrative directions that they might not have considered otherwise. Experimenting with open-ended prompts and letting the model's creativity shine can lead to exciting and serendipitous results.
Updated Invalid Date
📉
LLaMA2-13B-Tiefighter-GGUF
64
The LLaMA2-13B-Tiefighter-GGUF model is a large language model created by KoboldAI, a community group focused on developing advanced AI models. This model is a merged model, combining several different models and finetuning efforts to create a versatile and capable text generation model. It is based on the original LLaMA model, but has been further tuned and enhanced. Model inputs and outputs Inputs Text**: The model takes in natural language text as input, which it can then use to generate new text. Outputs Text**: The primary output of the model is generated text, which can be used for a variety of tasks such as creative writing, dialogue generation, and language modeling. Capabilities The LLaMA2-13B-Tiefighter-GGUF model is a highly capable text generation model that can be used for a wide range of applications. It has been tuned on a variety of datasets, including adventure and storytelling datasets, which gives it strong capabilities in areas like creative writing and narrative generation. The model is also known for its ability to engage in open-ended dialogues and conversations, making it suitable for chatbot and virtual assistant applications. What can I use it for? The LLaMA2-13B-Tiefighter-GGUF model is a versatile tool that can be used for a variety of projects and applications. Some potential use cases include: Creative writing**: The model's strong narrative and storytelling capabilities make it well-suited for generating creative fiction, poetry, and other forms of written expression. Dialogue and conversation**: The model's ability to engage in open-ended dialogue makes it a good fit for chatbot and virtual assistant applications, where it can provide natural and context-appropriate responses. Language modeling**: As a large language model, LLaMA2-13B-Tiefighter-GGUF can be used as a base for fine-tuning on specific tasks or domains, or for generating synthetic text for a variety of applications. Things to try One interesting aspect of the LLaMA2-13B-Tiefighter-GGUF model is its ability to engage in adventuring and adventure game-like behaviors. By providing a small introduction to a world and an objective, and using the '>' prefix for user commands, the model can be prompted to enter an "adventure mode" and respond accordingly, generating text that follows the narrative and progression of the adventure. Additionally, the model's diverse training data and finetuning efforts have resulted in a broad range of capabilities, from creative writing to factual knowledge. Experimenting with different prompts and instruction formats can help uncover the model's strengths and discover new and interesting ways to utilize its capabilities.
Updated Invalid Date
llamaguard-7b
540
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.
Updated Invalid Date
claude2-alpaca-13b
3
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.
Updated Invalid Date