CapybaraHermes-2.5-Mistral-7B-GPTQ
Maintainer: TheBloke
50
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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
CapybaraHermes-2.5-Mistral-7B-GPTQ is a large language model created by Argilla and quantized using GPTQ methods by TheBloke. It is based on the original CapybaraHermes-2.5-Mistral-7B model, which was a preference-tuned version of the OpenHermes-2.5-Mistral-7B model. The GPTQ quantization allows for reduced memory usage and faster inference on a variety of hardware. Compared to the similar CapybaraHermes-2.5-Mistral-7B-GGUF model, the GPTQ version provides a range of bit-depth options to balance model size, speed, and quality.
Model inputs and outputs
CapybaraHermes-2.5-Mistral-7B-GPTQ is a text-to-text model, meaning it takes in text prompts and generates text outputs. The model uses a special prompt format called ChatML, which includes system and user message tokens to structure the conversation.
Inputs
- Text prompts in the ChatML format, with
<|im_start|>system,<|im_end|>,<|im_start|>user, and<|im_end|>tokens.
Outputs
- Text continuations and responses generated by the model, in the same ChatML format.
Capabilities
The CapybaraHermes-2.5-Mistral-7B-GPTQ model is capable of engaging in open-ended dialogue, answering questions, and generating creative text on a wide range of topics. It has been shown to perform well on various benchmarks, including the AGI Evaluation, GPT4All, and BigBench tasks. The model can generate coherent and contextually appropriate responses, with capabilities that rival larger language models.
What can I use it for?
The versatile CapybaraHermes-2.5-Mistral-7B-GPTQ model can be used for a variety of natural language processing tasks, such as:
- Building interactive chatbots and conversational AI assistants
- Generating creative and informative text on demand
- Answering questions and providing information on a wide range of subjects
- Aiding in research and analysis by summarizing and synthesizing information
- Enhancing existing applications with intelligent language capabilities
The range of GPTQ quantization options provided makes this model suitable for deployment on a variety of hardware, from high-end GPUs to less powerful devices.
Things to try
One interesting aspect of the CapybaraHermes-2.5-Mistral-7B-GPTQ model is the ability to explore the different GPTQ quantization options. By trying out the various bit-depth and parameter configurations, you can find the right balance between model size, inference speed, and output quality for your specific use case and hardware. Additionally, the model's strong performance on multi-turn dialogue benchmarks suggests it may be well-suited for building engaging, context-aware conversational AI 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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