dolphin-2.6-mistral-7B-GGUF
Maintainer: TheBloke
68
🔎
| 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 |
Create account to get full access
Model overview
The dolphin-2.6-mistral-7B-GGUF is a large language model created by Cognitive Computations and maintained by TheBloke. It is an extension of the original Dolphin 2.6 Mistral 7B model, with the weights quantized into a GGUF format for improved performance and efficiency.
The model is part of TheBloke's collection of quantized AI models, which also includes the dolphin-2_6-phi-2-GGUF and dolphin-2.5-mixtral-8x7b-GGUF models. These quantized versions offer a range of trade-offs between model size, performance, and quality, allowing users to choose the best option for their specific needs and hardware capabilities.
Model inputs and outputs
Inputs
- Freeform natural language text prompts
Outputs
- Freeform natural language text completions, continuing the provided prompt
Capabilities
The dolphin-2.6-mistral-7B-GGUF model is a powerful text generation model capable of producing human-like responses on a wide range of topics. It can be used for tasks such as creative writing, Q&A, summarization, and open-ended conversation. The model's quantization into the GGUF format allows for faster inference and reduced memory usage, making it suitable for deployment on a variety of hardware platforms.
What can I use it for?
The dolphin-2.6-mistral-7B-GGUF model can be used in a variety of applications, such as:
- Content Generation: Use the model to generate original text for blog posts, social media updates, or other written content.
- Chatbots and Virtual Assistants: Integrate the model into chatbots or virtual assistants to provide natural language interactions.
- Language Modeling: Fine-tune the model on domain-specific data to create custom language models for specialized applications.
- Research and Experimentation: Explore the model's capabilities and limitations, and use it as a foundation for further AI research and development.
Things to try
One interesting aspect of the dolphin-2.6-mistral-7B-GGUF model is its ability to handle longer input sequences and generate coherent, context-aware responses. Try providing the model with prompts that span multiple sentences or paragraphs, and see how it can maintain the flow and relevance of the generated text. Additionally, experiment with different sampling techniques, such as temperature and top-k/top-p adjustments, to find the optimal balance between creativity and coherence in the model's outputs.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🐍
dolphin-2.2.1-mistral-7B-GGUF
115
The dolphin-2.2.1-mistral-7B-GGUF is an AI model created by Eric Hartford, a prominent AI model developer. It is based on the Dolphin 2.2.1 Mistral 7B model and has been converted to the GGUF format, a new model format introduced by the llama.cpp team. The model is supported by a range of AI clients and libraries, including llama.cpp, text-generation-webui, and KoboldCpp. These tools provide GPU acceleration and other advanced features for running the model efficiently. Model inputs and outputs Inputs Text prompt**: The model takes in a text prompt as input, which can be in the ChatML format with ` and ` tokens. Outputs Generated text**: The model outputs generated text based on the input prompt. The length of the generated text can be controlled through parameters like max_tokens. Capabilities The dolphin-2.2.1-mistral-7B-GGUF model is a capable text-to-text transformer that can perform a variety of tasks, such as answering questions, generating coherent stories, and providing detailed code samples. It has been trained on a diverse dataset and is particularly skilled at coding tasks, demonstrating strong programming abilities. What can I use it for? The dolphin-2.2.1-mistral-7B-GGUF model can be used for a wide range of applications, such as building chatbots, automating content generation, and assisting with programming and software development. Its strong coding capabilities make it well-suited for tasks like generating code snippets, explaining programming concepts, and even solving complex algorithmic problems. Things to try One interesting thing to try with the dolphin-2.2.1-mistral-7B-GGUF model is to explore its abilities in generating creative and imaginative stories. Provide the model with a simple prompt, such as "Once upon a time, in a faraway land...", and see how it expands the narrative and develops the story. You can also experiment with different genres, tones, and narrative perspectives to see the model's versatility. Another interesting area to explore is the model's ability to assist with programming tasks. Try providing it with a high-level description of a programming problem and see how it can break down the problem, propose solutions, and even generate the necessary code. This can be a valuable tool for developers, especially when working on complex or unfamiliar coding challenges.
Updated Invalid Date
⚙️
dolphin-2.0-mistral-7B-GGUF
48
The dolphin-2.0-mistral-7B-GGUF is a large language model created by Eric Hartford and maintained by TheBloke. It is based on the original Dolphin 2.0 Mistral 7B model, which was trained on a dataset curated by Hartford. This model is available in GGUF format, a new model format introduced by the llama.cpp team that replaces the older GGML format. Similar models in the Dolphin series include the dolphin-2.2.1-mistral-7B-GGUF and dolphin-2.1-mistral-7B-GGUF, which offer incremental improvements and updates over the original Dolphin 2.0 model. Model inputs and outputs The dolphin-2.0-mistral-7B-GGUF model takes natural language inputs and generates coherent text outputs. It uses the ChatML prompt format, which includes system and user message segments. Inputs Prompts**: Natural language prompts or messages from the user Outputs Text generation**: The model generates relevant and coherent text in response to the input prompts Capabilities The dolphin-2.0-mistral-7B-GGUF model is capable of a wide range of text-to-text tasks, such as language translation, question answering, summarization, and open-ended conversation. It has been trained on a large and diverse dataset, giving it broad knowledge and capabilities. One notable capability of this model is its ability to engage in multi-turn conversations. It can understand and respond to context, allowing for more natural and coherent dialogue. What can I use it for? The dolphin-2.0-mistral-7B-GGUF model can be used for a variety of applications that require natural language processing, such as: Chatbots and virtual assistants**: The model's conversation capabilities make it well-suited for building chatbots and virtual assistants that can engage in natural dialogue. Content generation**: The model can be used to generate text for a wide range of applications, such as articles, stories, or creative writing. Question answering**: The model can be used to build systems that can answer questions and provide information to users. Language translation**: While not specifically designed for translation, the model's language understanding capabilities could be leveraged for translation tasks. Things to try One interesting aspect of the dolphin-2.0-mistral-7B-GGUF model is its uncensored nature. The model has been trained on a dataset that has been filtered to remove alignment and bias, making it more compliant but also potentially less constrained in its outputs. This could be useful for certain applications, but users should be aware of the potential risks and take appropriate measures to ensure the model is used responsibly. Another thing to try with this model is exploring its multi-turn conversation capabilities. By engaging the model in a series of back-and-forth messages, you can see how it maintains context and provides coherent responses over the course of a longer dialogue. Overall, the dolphin-2.0-mistral-7B-GGUF model appears to be a powerful and versatile language model with a wide range of potential applications. Its GGUF format and support for a variety of client libraries and tools make it accessible and easy to integrate into various projects.
Updated Invalid Date
🤯
dolphin-2.6-mixtral-8x7b-GGUF
45
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.
Updated Invalid Date
🤔
dolphin-2_6-phi-2-GGUF
68
The dolphin-2_6-phi-2-GGUF is an AI model created by Cognitive Computations and provided in GGUF format by TheBloke. It is based on the Dolphin 2.6 Phi 2 model and has been quantized using hardware provided by Massed Compute. The GGUF format is a new model format introduced by the llama.cpp team as a replacement for GGML, which is no longer supported. Similar models include the dolphin-2.5-mixtral-8x7b-GGUF from Eric Hartford, the phi-2-GGUF from Microsoft, the Llama-2-7B-Chat-GGUF from Meta Llama 2, and the Mistral-7B-OpenOrca-GGUF from OpenOrca. Model inputs and outputs Inputs Text prompts in various formats including question-answer, chat, and code Outputs Generated text in response to the input prompt Capabilities The dolphin-2_6-phi-2-GGUF model is capable of a variety of natural language processing tasks such as question answering, dialogue, and code generation. It has been shown to perform well on benchmarks testing commonsense reasoning, world knowledge, and reading comprehension. What can I use it for? The dolphin-2_6-phi-2-GGUF model can be used for a variety of applications that require natural language processing, such as virtual assistants, chatbots, and code generation tools. Its strong performance on benchmark tasks suggests it could be a useful tool for researchers and developers working on language-based AI systems. Things to try One interesting thing to try with the dolphin-2_6-phi-2-GGUF model is using it for open-ended creative writing tasks. The model's strong language understanding capabilities could allow it to generate coherent and imaginative stories or poems in response to prompts. Developers could also experiment with using the model for task-oriented dialogue, such as helping users find information or complete specific tasks.
Updated Invalid Date