llama-3-cat-8b-instruct-v1
Maintainer: SteelStorage
47
🗣️
| 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 |
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Model Overview
llama-3-cat-8b-instruct-v1 is a variant of the Llama 3 language model developed by SteelStorage, a researcher on the Hugging Face platform. This model is an 8 billion parameter version of the Llama 3 language model that has been fine-tuned for instruction following, helpfulness, and character engagement.
The model was developed by a team including dataset builder Dr. Kal'tsit, trainer/funder SteelSkull, and facilitator Potatooff. It was trained using a combination of techniques including supervised fine-tuning, rejection sampling, proximal policy optimization, and direct policy optimization. The training data includes high-quality instruction-response pairs from the Hugging Face dataset as well as specialized health-related data from the Chat Doctor dataset.
Similar models include the 70B variant of this model developed by Dr. Kal'tsit and posted by Turboderp, as well as other Llama 3 models from the Meta-Llama project.
Model Inputs and Outputs
Inputs
- Text prompt containing instructions, context, and/or a query
Outputs
- Generated text response that follows the provided instructions and context, demonstrating helpfulness and character engagement
Capabilities
The llama-3-cat-8b-instruct-v1 model is particularly adept at:
- Faithfully following system prompts and instructions
- Engaging in multi-step "chain of thought" reasoning to solve complex tasks
- Immersing the user in a character or role-playing scenario
- Providing helpful information on topics like biosciences and general science
What Can I Use It For?
This model could be useful for a variety of applications that require an AI assistant to be highly responsive to instructions, helpful, and engaging. Some potential use cases include:
- Virtual assistant for customer service or research support
- Interactive educational or training tool
- Creative writing aid or story generation
- Scientific research and analysis assistant
SteelStorage's profile on Hugging Face provides more information on the researchers behind this model.
Things to Try
One interesting aspect of this model is its ability to provide detailed "chain of thought" explanations as it solves complex tasks. You could try giving it challenging prompts that require multi-step reasoning, and observe how it walks through the problem-solving process. Additionally, experimenting with different system prompt setups could allow you to explore the model's capacity for character immersion and role-playing.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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llama-3-cat-8b-instruct-v1
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Cat-Llama-3-70B-instruct
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Cat-llama3-instruct is a large language model developed by maintainer turboderp. It is a fine-tuned version of the Llama 3 70B model, with a focus on system prompt fidelity, helpfulness, and character engagement. The model aims to respect the system prompt to an extreme degree, provide helpful information regardless of the situation, and offer maximum character immersion (role-play) in given scenes. Compared to similar models like Meta-Llama-3-70B-Instruct and Llama-2-7B-32K-Instruct, Cat-llama3-instruct focuses more on system prompt fidelity and character engagement, while the others may be more broadly capable. Model Inputs and Outputs Inputs Text prompt provided to the model Outputs Text generated by the model in response to the input prompt Capabilities Cat-llama3-instruct excels at following system prompts and maintaining character immersion, while also providing helpful and informative responses. For example, when given a prompt to roleplay as a pirate chatbot, the model generates coherent and consistent pirate-themed responses. It also demonstrates strong problem-solving and task-completion abilities, such as providing step-by-step instructions for a medical diagnosis. What Can I Use It For? Cat-llama3-instruct can be a powerful tool for building interactive chatbots, virtual assistants, or roleplaying experiences. Its focus on prompt fidelity and character engagement makes it well-suited for applications that require a high degree of user immersion, such as interactive fiction or educational simulations. Additionally, its helpfulness and task-completion abilities make it useful for general-purpose assistants that need to provide informative and actionable responses. Things to Try One interesting aspect of Cat-llama3-instruct is its ability to maintain a coherent persona and tone throughout a conversation. Try giving it a variety of prompts that require the model to roleplay different characters or scenarios, and see how well it is able to stay in character. You can also experiment with prompts that require the model to provide step-by-step instructions or detailed information on a topic, to see how its helpfulness and knowledge capabilities compare to other models.
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Meta-Llama-3-8B-Instruct-GGUF
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The Meta-Llama-3-8B-Instruct is a community model created by the lmstudio-community based on Meta's open-sourced Meta-Llama-3-8B-Instruct model. This 8 billion parameter model is an instruction-tuned version of the Llama 3 language model, optimized for dialogue and outperforming many open-source chat models. The model was developed by Meta with a focus on helpfulness and safety. Model Inputs and Outputs Inputs Text prompts Outputs Generated text responses Capabilities The Meta-Llama-3-8B-Instruct model excels at a variety of natural language tasks, including multi-turn conversations, general knowledge questions, and even coding. It is highly capable at following system prompts to produce the desired behavior. What Can I Use It For? The Meta-Llama-3-8B-Instruct model can be used for a wide range of applications, from building conversational AI assistants to generating content for creative projects. The model's instruction-following capabilities make it well-suited for use cases like customer support, virtual assistants, and even creative writing. Additionally, the model's strong performance on coding-related tasks suggests it could be useful for applications like code generation and programming assistance. Things to Try One interesting capability of the Meta-Llama-3-8B-Instruct model is its ability to adopt different personas and respond accordingly. By providing a system prompt that sets the model's role, such as "You are a pirate chatbot who always responds in pirate speak!", you can generate creative and engaging conversational outputs. Another interesting area to explore is the model's performance on complex reasoning and problem-solving tasks, where its strong knowledge base and instruction-following skills could prove valuable.
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llama-3-8b-Instruct
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llama-3-8b-Instruct is a large language model finetuned by Unsloth, a Hugging Face creator. It is based on the Llama-3 8B model and has been optimized for increased performance and reduced memory usage. Unsloth has developed notebooks that allow you to finetune the model 2-5x faster with 70% less memory, making it more accessible for a wider range of users and applications. Model inputs and outputs llama-3-8b-Instruct is a text-to-text model, capable of processing and generating natural language. It can be used for a variety of tasks, such as language modeling, text generation, and conversational AI. Inputs Natural language text Outputs Natural language text Capabilities The llama-3-8b-Instruct model has been finetuned to improve its performance and efficiency. Unsloth's notebooks allow you to finetune the model on your own dataset, resulting in a 2-5x speed increase and 70% reduction in memory usage compared to the original Llama-3 8B model. What can I use it for? The llama-3-8b-Instruct model can be used for a wide range of natural language processing tasks, such as text generation, language modeling, and conversational AI. Unsloth's finetuning process makes the model more accessible for a wider range of users and applications, as it can be deployed on less powerful hardware. Things to try You can use the provided Colab notebooks to finetune the llama-3-8b-Instruct model on your own dataset, which can then be exported and used in your own projects. Unsloth's optimization techniques allow for faster finetuning and more efficient model deployment, making it a versatile tool for natural language processing tasks.
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