laser-dolphin-mixtral-2x7b-dpo-GGUF
Maintainer: TheBloke
47
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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 laser-dolphin-mixtral-2x7b-dpo-GGUF model is a GGUF format variant of the Laser Dolphin Mixtral 2X7B DPO model created by tim. This model has been quantized using hardware provided by Massed Compute. It is one of several similar models maintained by TheBloke that utilize the GGUF format, a new replacement for GGML introduced by the llama.cpp team. Other similar models include the dolphin-2.7-mixtral-8x7b-GGUF and dolphin-2.5-mixtral-8x7b-GGUF.
Model inputs and outputs
The laser-dolphin-mixtral-2x7b-dpo-GGUF model uses the ChatML prompt format, which consists of a system message, user prompt, and assistant response. The model can accept a wide range of prompts and generate coherent, context-aware responses. Some key highlights include the model's strong capabilities in areas like code generation, task completion, and open-ended conversation.
Inputs
- System message: Provides context and instructions for the assistant
- User prompt: The query or task the user wants the assistant to address
Outputs
- Assistant response: The generated text response from the model, which aims to address the user's prompt while following the provided system instructions
Capabilities
The laser-dolphin-mixtral-2x7b-dpo-GGUF model is a capable language model that can assist with a variety of tasks. It demonstrates strong abilities in areas like code generation, task completion, and open-ended conversation. For example, the model can provide step-by-step instructions for training a dolphin, generate creative stories about llamas, or answer questions about theories of everything in physics.
What can I use it for?
The laser-dolphin-mixtral-2x7b-dpo-GGUF model could be useful for a range of applications, from building AI-powered chatbots and virtual assistants to automating content generation and task completion. Developers and researchers could leverage this model to create engaging, conversational experiences for users, or to build more intelligent systems that can understand and respond to natural language inputs. Additionally, the GGUF format of this model makes it compatible with a growing number of inference tools and platforms, including llama.cpp, text-generation-webui, and LM Studio.
Things to try
One interesting aspect of the laser-dolphin-mixtral-2x7b-dpo-GGUF model is its ability to handle long-form, open-ended prompts and engage in multi-turn conversations. Rather than just providing a single response, the model can maintain context and build upon previous exchanges, leading to more coherent and natural-sounding dialogue. Developers and users may want to experiment with prompting the model to have extended conversations on a variety of topics, or to break down complex tasks into a series of steps and have the model walk through the process.
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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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 user\n{prompt} tags. System message**: An optional system message that can be used to set the context or instructions for the model, enclosed in system\n{system_message} 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.
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dolphin-2.5-mixtral-8x7b-GGUF
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