Mixtral-8x22B-v0.1-GGUF
Maintainer: MaziyarPanahi
71
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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-8x22B-v0.1-GGUF is a large language model with 176B parameters, created by MistralAI. It is a sparse mixture of experts model that outperforms the 70B Llama 2 model on many benchmarks. The model is available in quantized GGUF format, which allows for efficient CPU and GPU inference.
Model inputs and outputs
Inputs
- Raw text prompts of varying lengths, up to 65,000 tokens
Outputs
- Continuation of the input text, generating coherent and contextual responses
- The model can be used for a variety of text generation tasks, such as story writing, question answering, and open-ended conversation
Capabilities
The Mixtral-8x22B-v0.1-GGUF model demonstrates strong performance on a range of benchmarks, including the AI2 Reasoning Challenge, HellaSwag, MMLU, TruthfulQA, Winogrande, and GSM8k. It is capable of generating human-like text across diverse domains and tasks.
What can I use it for?
The Mixtral-8x22B-v0.1-GGUF model can be used for a variety of natural language processing tasks, such as content generation, chatbots, and language modeling. Its large size and strong performance make it well-suited for applications that require sophisticated language understanding and generation, such as creative writing assistants, question-answering systems, and virtual assistants.
Things to try
Experiment with the model's ability to maintain coherence and context over long sequences of text. Try providing it with open-ended prompts and observe how it builds upon and develops the narrative. Additionally, you can fine-tune the model on specialized datasets to adapt it to specific domains or tasks, unlocking even more 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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