SynthIA-7B-v1.3
Maintainer: migtissera
142
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| 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
The SynthIA-7B-v1.3 is a Mistral-7B-v0.1 model trained on Orca style datasets. It has been fine-tuned for instruction following as well as having long-form conversations. The model is released by migtissera under the Apache 2.0 license.
Similar models include the neural-chat-7b-v3-1 and neural-chat-7b-v3-3 models, which are also fine-tuned 7B language models. However, the SynthIA-7B-v1.3 is focused on instruction following and open-ended conversations, rather than the more specialized tasks of those models.
Model inputs and outputs
Inputs
- Instruction: The model accepts instructions or prompts for the AI assistant to elaborate on using a Tree of Thoughts and Chain of Thought reasoning.
Outputs
- Natural language response: The model generates a coherent, step-by-step response that addresses the given instruction or prompt.
Capabilities
The SynthIA-7B-v1.3 model demonstrates strong capabilities in open-ended instruction following and long-form conversation. It can break down complex topics, explore relevant sub-topics, and construct a clear reasoning to answer questions or address prompts. The model's performance is evaluated to be on par with other leading 7B language models.
What can I use it for?
The SynthIA-7B-v1.3 model would be well-suited for applications that require an AI assistant to engage in substantive, multi-turn dialogues. This could include virtual agents, chatbots, or question-answering systems that need to provide detailed, thoughtful responses. The model's ability to follow instructions and reason through problems makes it a good fit for educational or research applications as well.
Things to try
One interesting aspect of the SynthIA-7B-v1.3 model is its use of a "Tree of Thoughts" and "Chain of Thought" reasoning approach. You could experiment with prompts that ask the model to explicitly outline its step-by-step reasoning, exploring how it builds a logical flow of ideas to arrive at the final response. Additionally, you could test the model's ability to handle open-ended, multi-part instructions or prompts that require it to demonstrate flexible, contextual understanding.
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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