openchat_3.5-GGUF
Maintainer: TheBloke
125
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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
openchat_3.5-GGUF is a 7B parameter language model created by TheBloke and based on the OpenChat 3.5 model. It uses the new GGUF format, which offers advantages over the previous GGML format. The model has been quantized using hardware provided by Massed Compute, with a variety of quantization options available ranging from 2-bit to 8-bit. This allows for models tailored to different use cases in terms of size, speed, and quality tradeoffs.
Similar models available include the Llama-2-7B-Chat-GGUF, Llama-2-13B-chat-GGUF, and Llama-2-70B-Chat-GGUF models, also created by TheBloke.
Model inputs and outputs
openchat_3.5-GGUF is a text-to-text model, taking text as input and generating text as output. The model is optimized for dialogue and chat use cases.
Inputs
- Text prompt to continue or respond to
Outputs
- Continuation or response text generated by the model
Capabilities
openchat_3.5-GGUF is capable of engaging in dialogue, answering questions, and generating coherent and contextual responses. It has been fine-tuned on chat data to improve its performance in interactive conversation. The model can handle a wide range of topics and tasks, from open-ended discussions to task-oriented exchanges.
What can I use it for?
openchat_3.5-GGUF can be used to build chat-based AI assistants, language generation tools, and interactive applications. Its capabilities make it well-suited for customer service, educational applications, creative writing assistance, and more. The model's quantization options allow users to find the right balance between model size, speed, and quality for their specific use case.
Things to try
One interesting aspect of openchat_3.5-GGUF is its ability to handle extended sequences, with the necessary RoPE scaling parameters automatically read from the GGUF files and set by the llama.cpp library. This allows for generation of longer and more coherent responses, which could be useful for tasks like story generation or task-oriented dialogue.
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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