wizard-mega-13b-awq
Maintainer: nateraw
5
| 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
wizard-mega-13b-awq is a large language model (LLM) developed by nateraw that has been quantized using Adaptive Weight Quantization (AWQ) and served with vLLM. It is similar in capabilities to other LLMs like nous-hermes-llama2-awq, wizardlm-2-8x22b, and whisper-large-v3. These models can be used for a variety of language-based tasks such as text generation, question answering, and language translation.
Model inputs and outputs
wizard-mega-13b-awq takes in a text prompt and generates additional text based on that prompt. The model allows you to control various parameters like the "top k" and "top p" values, temperature, and the maximum number of new tokens to generate. The output is a string of generated text.
Inputs
- message: The text prompt to be used as input for the model.
- max_new_tokens: The maximum number of tokens the model should generate as output.
- temperature: The value used to modulate the next token probabilities.
- top_p: A probability threshold for generating the output. If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering).
- top_k: The number of highest probability tokens to consider for generating the output. If > 0, only keep the top k tokens with highest probability (top-k filtering).
- presence_penalty: The presence penalty.
Outputs
- Output: The generated text output from the model.
Capabilities
wizard-mega-13b-awq is a powerful language model that can be used for a variety of tasks. It can generate coherent and contextually-appropriate text, answer questions, and even engage in open-ended conversations. The model has been trained on a vast amount of text data, giving it a broad knowledge base that it can draw upon.
What can I use it for?
wizard-mega-13b-awq can be used for a wide range of applications, such as:
- Content generation: The model can be used to generate articles, stories, or other types of written content.
- Chatbots and virtual assistants: The model can be used to power conversational AI agents that can engage in natural language interactions.
- Language translation: The model can be fine-tuned for translation tasks, allowing it to translate text between different languages.
- Question answering: The model can be used to answer questions on a variety of topics, drawing upon its broad knowledge base.
Things to try
One interesting thing to try with wizard-mega-13b-awq is to experiment with the different input parameters, such as temperature and top-k/top-p values. Adjusting these can result in significantly different output styles, from more creative and diverse to more conservative and coherent. You can also try prompting the model with open-ended questions or tasks and see how it responds, as this can reveal interesting insights about its capabilities and limitations.
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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