dolphin-2.1-mistral-7b
Maintainer: lucataco
12
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
The dolphin-2.1-mistral-7b model is a fine-tuned version of the Mistral-7B language model, trained on the Dolphin dataset. It is maintained by lucataco. This model is similar to other Mistral-7B models, such as mistral-7b-v0.1, mistral-7b-instruct-v0.2, and mistral-7b-instruct-v0.1, all of which are large language models from Mistral.
Model inputs and outputs
The dolphin-2.1-mistral-7b model takes a text prompt as input and generates a text output. The input prompt can be customized using various parameters, including top_k, top_p, temperature, max_new_tokens, prompt_template, presence_penalty, and frequency_penalty.
Inputs
- prompt: The input text prompt.
- 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).
- top_p: A probability threshold for generating the output. If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering).
- temperature: The value used to modulate the next token probabilities.
- max_new_tokens: The maximum number of tokens the model should generate as output.
- prompt_template: The template used to format the prompt. The input prompt is inserted into the template using the
{prompt}placeholder. - presence_penalty: The presence penalty.
- frequency_penalty: The frequency penalty.
Outputs
- The model generates a sequence of text as the output.
Capabilities
The dolphin-2.1-mistral-7b model is capable of generating high-quality and coherent text based on the provided input prompt. It can be used for a variety of natural language processing tasks, such as conversational AI, text generation, and language modeling.
What can I use it for?
The dolphin-2.1-mistral-7b model can be used for various applications, such as building chatbots, generating custom content, and assisting with language-based tasks. For example, you could use this model to create a conversational AI assistant that can engage in natural conversations, answer questions, and provide helpful information.
Things to try
With the dolphin-2.1-mistral-7b model, you can experiment with different input prompts and parameters to see how the model responds. Try adjusting the temperature, top_k, and top_p parameters to see how they affect the model's output. You can also explore using the model in combination with other tools and technologies, such as natural language processing libraries or text-to-speech systems like whisperspeech-small, to create more complex applications.
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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