Mixtral-8x7B-Instruct-v0.1-GPTQ
Maintainer: TheBloke
124
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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 Mixtral-8x7B-Instruct-v0.1-GPTQ is a large language model created by Mistral AI_ and maintained by TheBloke. It is an 8 billion parameter model that has been fine-tuned for instruction following, outperforming the Llama 2 70B model on many benchmarks. This model is available in various quantized formats, including GPTQ, which reduces the memory footprint for GPU inference. The GPTQ versions provided offer a range of bit sizes and quantization parameters to choose from, allowing users to balance model quality and performance requirements.
Model inputs and outputs
Inputs
- Prompts: The model takes instruction-based prompts as input, following a specific template format of
[INST] {prompt} [/INST].
Outputs
- Responses: The model generates coherent and relevant responses based on the provided instruction prompts. The responses continue the conversational flow and aim to address the user's request.
Capabilities
The Mixtral-8x7B-Instruct-v0.1-GPTQ model is capable of a wide range of language tasks, including text generation, question answering, summarization, and task completion. It has been designed to excel at following instructions and engaging in interactive, multi-turn dialogues. The model can generate human-like responses, drawing upon its broad knowledge base to provide informative and contextually appropriate outputs.
What can I use it for?
The Mixtral-8x7B-Instruct-v0.1-GPTQ model can be used for a variety of applications, such as building interactive AI assistants, automating content creation workflows, and enhancing customer support experiences. Its instruction-following capabilities make it well-suited for task-oriented applications, where users can provide step-by-step instructions and the model can respond accordingly. Potential use cases include virtual personal assistants, automated writing tools, and task automation in various industries.
Things to try
One interesting aspect of the Mixtral-8x7B-Instruct-v0.1-GPTQ model is its ability to engage in multi-turn dialogues and maintain context throughout a conversation. Users can experiment with providing follow-up instructions or clarifications to the model and observe how it adapts its responses to maintain coherence and address the updated requirements. Additionally, users can explore the model's versatility by testing it on a diverse range of tasks, from creative writing to analytical problem-solving, to fully appreciate the breadth of its capabilities.
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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