zephyr-7b-alpha
Maintainer: joehoover
6
| Property | Value |
|---|---|
| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| Paper Link | View on Arxiv |
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Model overview
The zephyr-7b-alpha is a high-performing language model developed by Replicate and maintained by joehoover. It is part of the Zephyr series of models, which are trained to act as helpful assistants. This model is similar to other Zephyr models like zephyr-7b-beta and zephyr-7b-beta, as well as the falcon-40b-instruct model also maintained by joehoover.
Model inputs and outputs
The zephyr-7b-alpha model takes in a variety of inputs to control the generation process, including a prompt, system prompt, temperature, top-k and top-p sampling parameters, and more. The model produces an array of text as output, with the option to return only the logits for the first token.
Inputs
- Prompt: The prompt to send to the model.
- System Prompt: A system prompt that is prepended to the user prompt to help guide the model's behavior.
- Temperature: Adjusts the randomness of the outputs, with higher values being more random and lower values being more deterministic.
- Top K: When decoding text, samples from the top k most likely tokens, ignoring less likely tokens.
- Top P: When decoding text, samples from the top p percentage of most likely tokens, ignoring less likely tokens.
- Max New Tokens: The maximum number of tokens to generate.
- Min New Tokens: The minimum number of tokens to generate (or -1 to disable).
- Stop Sequences: A comma-separated list of sequences to stop generation at.
- Seed: A random seed to use for generation (leave blank to randomize).
- Debug: Whether to provide debugging output in the logs.
- Return Logits: Whether to only return the logits for the first token (for testing purposes).
- Replicate Weights: The path to fine-tuned weights produced by a Replicate fine-tune job.
Outputs
- An array of generated text.
Capabilities
The zephyr-7b-alpha model is capable of generating high-quality, coherent text across a variety of domains. It can be used for tasks like content creation, question answering, and task completion. The model has been trained to be helpful and informative, making it a useful tool for a wide range of applications.
What can I use it for?
The zephyr-7b-alpha model can be used for a variety of applications, such as content creation for blogs, articles, or social media posts, question answering to provide helpful information to users, and task completion to automate various workflows. The model's capabilities can be further enhanced through fine-tuning on specific datasets or tasks.
Things to try
Some ideas to try with the zephyr-7b-alpha model include generating creative stories, summarizing long-form content, or providing helpful advice and recommendations. The model's flexibility and strong language understanding make it a versatile tool for a wide range of use cases.
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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