cosmo-1b
Maintainer: HuggingFaceTB
117
✅
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 cosmo-1b
model is a 1.8B parameter language model trained by HuggingFaceTB on a synthetic dataset called Cosmopedia. The training corpus consisted of 30B tokens, 25B of which were synthetic from Cosmopedia, augmented with 5B tokens from sources like AutoMathText and The Stack. The model uses the tokenizer from the Mistral-7B-v0.1 model.
Model Inputs and Outputs
The cosmo-1b
model is a text-to-text AI model, meaning it can take textual input and generate textual output.
Inputs
- Text prompts that the model uses to generate new text.
Outputs
- Generated text based on the input prompt.
Capabilities
The cosmo-1b
model is capable of generating coherent and relevant text in response to given prompts. While it was not explicitly instruction-tuned, the inclusion of the UltraChat dataset in pretraining allows it to be used in a chat-like format. The model can generate stories, explain concepts, and provide informative responses to a variety of prompts.
What Can I Use It For?
The cosmo-1b
model could be useful for various text generation tasks, such as:
- Creative writing: The model can be used to generate stories, dialogues, or creative pieces of text.
- Educational content creation: The model can be used to generate explanations, tutorials, or summaries of concepts.
- Chatbot development: The model's chat-like capabilities could be leveraged to build conversational AI assistants.
Things to Try
Some interesting things to try with the cosmo-1b
model include:
- Experimenting with different prompts to see the range of text the model can generate.
- Evaluating the model's performance on specific tasks, such as generating coherent stories or explaining complex topics.
- Exploring the model's ability to handle long-form text generation and maintain consistency over extended passages.
- Investigating the model's potential biases or limitations by testing it on a diverse set of inputs.
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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