Mistral-7B-Instruct-v0.2-GPTQ
Maintainer: TheBloke
45
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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
The Mistral-7B-Instruct-v0.2-GPTQ model is a version of the Mistral 7B Instruct model that has been quantized using GPTQ techniques. It was created by TheBloke, who has also produced several similar quantized models for the Mistral 7B Instruct and Mixtral 8x7B models. These quantized models provide more efficient inference by reducing the model size and memory requirements, while aiming to preserve as much quality as possible.
Model inputs and outputs
Inputs
- Prompt: The model expects prompts to be formatted with the
<s>[INST] {prompt} [/INST]template. This signifies the beginning of an instruction which the model should try to follow.
Outputs
- Generated text: The model will generate text in response to the provided prompt, ending the output when it encounters the end-of-sentence token.
Capabilities
The Mistral-7B-Instruct-v0.2-GPTQ model is capable of performing a variety of language tasks such as answering questions, generating coherent text, and following instructions. It can be used for applications like dialogue systems, content generation, and text summarization. The model has been fine-tuned on a range of datasets to develop its instructional capabilities.
What can I use it for?
The Mistral-7B-Instruct-v0.2-GPTQ model could be useful for a variety of applications that require language understanding and generation, such as:
- Chatbots and virtual assistants: The model's ability to follow instructions and engage in dialogue makes it well-suited for building conversational AI systems.
- Content creation: The model can be used to generate text, stories, or other creative content.
- Question answering: The model can be prompted to answer questions on a wide range of topics.
- Text summarization: The model could be used to generate concise summaries of longer passages of text.
Things to try
Some interesting things to try with the Mistral-7B-Instruct-v0.2-GPTQ model include:
- Experimenting with different prompting strategies to see how the model responds to more open-ended or complex instructions.
- Combining the model with other techniques like few-shot learning or fine-tuning to further enhance its capabilities.
- Exploring the model's limits by pushing it to generate text on more specialized or technical topics.
- Analyzing the model's responses to better understand its strengths, weaknesses, and biases.
Overall, the Mistral-7B-Instruct-v0.2-GPTQ model provides a powerful and versatile language generation capability that could be valuable for a wide range of 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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