Yi-34B-Llama
Maintainer: chargoddard
56
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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
[object Object] is an AI model that has been derived from the Llama language model developed by the FAIR team at Meta AI. The model has had its tensors renamed to match the standard Llama modeling code, allowing it to be loaded without the need for trust_remote_code. The llama-tokenizer branch also uses the Llama tokenizer class. This model shares similarities with other Llama-based models like [object Object], [object Object], [object Object], [object Object], and [object Object], all of which are based on the Llama architecture.
Model inputs and outputs
Yi-34B-Llama is a text-to-text model, meaning it takes text as input and generates text as output. The model can be used for a variety of natural language processing tasks, such as language generation, question answering, and text summarization.
Inputs
- Text prompts that the model can use to generate output
Outputs
- Generated text based on the input prompts
Capabilities
Yi-34B-Llama can be used for a variety of text-based tasks, such as generating coherent and contextual responses to prompts, answering questions, and summarizing text. The model has been trained on a large corpus of text data and can leverage its knowledge to produce human-like outputs.
What can I use it for?
The Yi-34B-Llama model can be used for a wide range of applications, such as chatbots, content generation, and language understanding. Researchers and developers can use this model as a starting point for building more specialized AI systems or fine-tuning it on specific tasks. The model's capabilities make it a useful tool for projects involving natural language processing and generation.
Things to try
Researchers and developers can experiment with the Yi-34B-Llama model by prompting it with different types of text and evaluating its performance on various tasks. They can also explore ways to fine-tune or adapt the model to their specific needs, such as by incorporating additional training data or adjusting the model architecture.
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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