dolphin-2.6-mixtral-8x7b-GGUF
Maintainer: TheBloke
45
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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 dolphin-2.6-mixtral-8x7b-GGUF model is a large language model created by Cognitive Computations and maintained by TheBloke. It is an update to the Dolphin 2.5 and 2.6 models, with improvements to the transformers library and model architecture. The model is based on the Mixtral-8x7b base and has been trained on a large dataset focused on coding, making it well-suited for tasks like code generation and programming assistance. Similar models maintained by TheBloke include the dolphin-2.7-mixtral-8x7b-GGUF and dolphin-2.6-mistral-7B-GGUF.
Model inputs and outputs
The dolphin-2.6-mixtral-8x7b-GGUF model accepts text inputs in a ChatML format, with the prompt structured as a conversation between the user and the assistant. The model can generate coherent, contextual responses to a wide range of prompts, from open-ended questions to specific task requests.
Inputs
- Prompt: A text prompt in ChatML format, with the user's input enclosed in
<|im_start|>user\n{prompt}<|im_end|>tags. - System message: An optional system message that can be used to set the context or instructions for the model, enclosed in
<|im_start|>system\n{system_message}<|im_end|>tags.
Outputs
- Generated text: The model's response to the input prompt, which can be of varying length depending on the task.
Capabilities
The dolphin-2.6-mixtral-8x7b-GGUF model excels at tasks that require strong coding and programming abilities, such as generating and explaining code snippets, providing code suggestions and solutions, and assisting with software development tasks. It can also engage in open-ended conversations on a variety of topics, drawing upon its broad knowledge base.
What can I use it for?
The dolphin-2.6-mixtral-8x7b-GGUF model can be a valuable tool for developers, programmers, and anyone working on software-related projects. It can be used to:
- Generate and explain code snippets
- Provide code suggestions and solutions
- Assist with software development tasks
- Engage in open-ended conversations on technical topics
Additionally, the model's broad knowledge base makes it suitable for other applications, such as content creation, research assistance, and general language understanding.
Things to try
One interesting aspect of the dolphin-2.6-mixtral-8x7b-GGUF model is its ability to handle extended sequence lengths, thanks to the RoPE scaling parameters built into the GGUF format. This allows you to generate longer, more coherent responses for tasks like story writing or other creative applications. You can experiment with increasing the sequence length (using the -c parameter in llama.cpp) to see how the model's performance and output changes.
Another useful feature is the model's support for GPU offloading, which can significantly improve performance and reduce memory usage. You can adjust the number of layers offloaded to the GPU using the -ngl parameter in llama.cpp to find the optimal balance between speed and resource usage for your specific hardware and application.
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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