nous-hermes-llama2-awq
Maintainer: nateraw
7
⚙️
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | No paper link provided |
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Model overview
nous-hermes-llama2-awq is a language model based on the Llama 2 architecture, developed by nateraw. It is a "vLLM" (virtualized Large Language Model) version of the Nous Hermes Llama2-AWQ model, providing an open source and customizable interface for using the model.
The model is similar to other Llama-based models like the llama-2-7b, nous-hermes-2-solar-10.7b, meta-llama-3-70b, and goliath-120b, which are large language models with a range of capabilities.
Model inputs and outputs
The nous-hermes-llama2-awq model takes a prompt as input and generates text as output. The prompt is used to guide the model's generation, and the model outputs a sequence of text based on the prompt.
Inputs
- Prompt: The text that is used to initiate the model's generation.
- Top K: The number of highest probability tokens to consider for generating the output.
- Top P: A probability threshold for generating the output, where only the top tokens with cumulative probability above this threshold are considered.
- Temperature: A value used to modulate the next token probabilities, controlling the creativity and randomness of the output.
- Max New Tokens: The maximum number of tokens the model should generate as output.
- Prompt Template: A template used to format the prompt, with a
{prompt}placeholder for the input prompt. - Presence Penalty: A penalty applied to tokens that have already appeared in the output, to encourage diversity.
- Frequency Penalty: A penalty applied to tokens based on their frequency in the output, to discourage repetition.
Outputs
- The model outputs a sequence of text, with each element in the output array representing a generated token.
Capabilities
The nous-hermes-llama2-awq model is a powerful language model capable of generating human-like text across a wide range of domains. It can be used for tasks such as text generation, dialogue, and summarization, among others. The model's performance can be fine-tuned for specific use cases by adjusting the input parameters.
What can I use it for?
The nous-hermes-llama2-awq model can be useful for a variety of applications, such as:
- Content Generation: Generating articles, stories, or other textual content. The model's ability to generate coherent and contextual text can be leveraged for tasks like creative writing, blog post generation, and more.
- Dialogue Systems: Building chatbots and virtual assistants that can engage in natural conversations. The model's language understanding and generation capabilities make it well-suited for this task.
- Summarization: Automatically summarizing long-form text, such as news articles or research papers, to extract the key points.
- Question Answering: Providing answers to questions based on the provided prompt and the model's knowledge.
Things to try
Some interesting things to try with the nous-hermes-llama2-awq model include:
- Experimenting with different prompt templates and input parameters to see how they affect the model's output.
- Trying the model on a variety of tasks, such as generating product descriptions, writing poetry, or answering open-ended questions, to explore its versatility.
- Comparing the model's performance to other similar language models, such as the ones mentioned in the "Model overview" section, to understand its relative strengths and weaknesses.
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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