oasst-sft-7-llama-30b-xor
Maintainer: OpenAssistant
63
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| Property | Value |
|---|---|
| Model Link | View 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
The oasst-sft-7-llama-30b-xor model is an open-source language model developed by OpenAssistant. It is a fine-tuned version of Meta AI's LLaMA 30B model, with the original LLaMA weights converted to an XOR format due to licensing restrictions. This process enables the distribution of LLaMA-based models while respecting the original model's licensing terms.
Similar models include the oasst-sft-6-llama-30b-xor and llama2-70b-oasst-sft-v10 models, which are also fine-tuned versions of LLaMA-based models by OpenAssistant.
Model Inputs and Outputs
The oasst-sft-7-llama-30b-xor model is a causal language model, which means it generates text one token at a time, conditioning on the previous tokens. The model takes as input a sequence of text tokens and generates a continuation of that sequence.
Inputs
- A sequence of text tokens
Outputs
- A continuation of the input text, generated one token at a time
Capabilities
The oasst-sft-7-llama-30b-xor model can be used for a variety of natural language processing tasks, such as text generation, language understanding, and language translation. It has been trained on a diverse dataset, enabling it to generate coherent and contextually relevant text on a wide range of topics.
What Can I Use it For?
The oasst-sft-7-llama-30b-xor model can be used in a variety of applications, such as:
- Content generation: The model can be used to generate text for blogs, articles, stories, or other creative content.
- Chatbots and virtual assistants: The model can be fine-tuned or used as a base for building conversational AI systems.
- Language translation: The model can be fine-tuned for language translation tasks, leveraging its understanding of multiple languages.
- Text summarization: The model can be used to generate concise summaries of longer text.
Things to Try
Some interesting things to try with the oasst-sft-7-llama-30b-xor model include:
- Exploring the model's capabilities in different domains: Try prompting the model with topics or tasks outside of its training distribution, such as coding, math, or scientific writing, to see how it performs.
- Experimenting with prompt engineering: Craft different types of prompts, such as open-ended questions, instructions, or dialogue, to see how the model responds.
- Evaluating the model's safety and ethical considerations: Carefully test the model's outputs for potential biases, hallucinations, or other undesirable behaviors, and think about ways to mitigate these issues.
Overall, the oasst-sft-7-llama-30b-xor model is a powerful and flexible language model that can be leveraged for a wide range of natural language processing tasks. By exploring its capabilities and limitations, you can gain valuable insights and potentially develop innovative applications.
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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The codellama-13b-oasst-sft-v10 model is an Open-Assistant fine-tuning of Meta's CodeLlama 13B large language model (LLM). It was developed by the OpenAssistant team. This model is a continuation of the OpenAssistant project, which aims to create an open-sourced, safe, and useful AI assistant. Similar models from the OpenAssistant project include the StableLM-7B SFT-7 and LLAMA-30B SFT-6 models, which have also been fine-tuned on human-generated conversations to improve their performance on dialogue tasks. Model inputs and outputs Inputs The model takes text as input, which can include multiple turns of a conversation between a user and an assistant. Outputs The model generates text as output, continuing the conversation from the user's prompt. Capabilities The codellama-13b-oasst-sft-v10 model is capable of engaging in open-ended dialogue, answering questions, and generating informative and coherent text. It has been trained to provide helpful and safe responses, and can be used for a variety of language generation tasks. What can I use it for? The codellama-13b-oasst-sft-v10 model can be used to build conversational AI applications, such as virtual assistants, chatbots, and question-answering systems. It could also be fine-tuned further for specialized tasks, such as code generation, summarization, or creative writing, by training on domain-specific data. Things to try One interesting thing to try with the codellama-13b-oasst-sft-v10 model is to engage it in multi-turn conversations, where the model can demonstrate its ability to maintain context and provide consistent, coherent responses over the course of an exchange. Additionally, you could prompt the model with open-ended questions or tasks to see the breadth of its capabilities.
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