Falcon-180B-Chat-GPTQ
Maintainer: TheBloke
67
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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 Falcon-180B-Chat-GPTQ model is a 180 billion parameter causal decoder-only language model created by Technology Innovation Institute. It is based on the original Falcon-180B model and fine-tuned on a mixture of chat datasets. This quantized GPTQ version provides a range of options to balance inference quality and VRAM usage. Compared to other large language models, Falcon-180B-Chat outperforms models like LLaMA-2, StableLM, and RedPajama according to the OpenLLM Leaderboard.
Model inputs and outputs
Inputs
- Text: The
Falcon-180B-Chat-GPTQmodel takes text as input, which it uses to generate new text.
Outputs
- Text: The model outputs new text, continuing the provided input.
Capabilities
The Falcon-180B-Chat-GPTQ model is capable of generating human-like text across a variety of topics. It can engage in open-ended conversation, answer questions, and produce creative and coherent written content. The model's strong performance on benchmarks suggests it is one of the most capable open-source language models currently available.
What can I use it for?
The Falcon-180B-Chat-GPTQ model can be used for a wide range of natural language processing tasks, such as chatbots, question-answering systems, text summarization, and creative writing. Given its high performance, it could serve as a strong foundation for further fine-tuning and specialization to specific use cases. Developers and researchers may find it useful as a starting point for building advanced language AI applications.
Things to try
One interesting aspect of the Falcon-180B-Chat-GPTQ model is its ability to generate responses that maintain a consistent personality and tone, even across multiple exchanges. You could try providing the model with a short prompt that establishes a particular character or scenario, then see how it continues the conversation in a coherent and natural way. Another idea is to explore the model's performance on tasks that require reasoning, such as answering open-ended questions or solving simple logic problems - the model's strong performance on benchmarks suggests it may excel at these types of tasks as well.
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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