SOLAR-10.7B-Instruct-v1.0-GGUF
Maintainer: TheBloke
81
🤷
| 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 SOLAR-10.7B-Instruct-v1.0-GGUF is a large language model created by upstage and quantized by TheBloke. It is part of TheBloke's suite of quantized AI models available in the GGUF format, which is a new format introduced by the llama.cpp team to replace the older GGML format. The GGUF format offers advantages like better tokenization and support for special tokens.
This model is similar to other large language models like Deepseek Coder 6.7B Instruct and CodeLlama 7B Instruct, which are also available in quantized GGUF format from TheBloke. All these models are designed for general text generation and understanding, with a focus on tasks like code synthesis and completion.
Model inputs and outputs
Inputs
- Text: The model takes natural language text as input, which can include prompts, instructions, or conversational messages.
Outputs
- Text: The model generates natural language text in response to the input. This can include completions, answers, or continued dialogue.
Capabilities
The SOLAR-10.7B-Instruct-v1.0-GGUF model has broad capabilities in areas like text generation, language understanding, and task-oriented dialog. It can be used for a variety of applications, such as:
- Code generation and completion: The model can assist with writing and understanding code, suggesting completions, and explaining programming concepts.
- General language tasks: The model can be used for tasks like text summarization, question answering, and creative writing.
- Conversational AI: The model can engage in open-ended dialogue, following instructions, and providing helpful responses.
What can I use it for?
The SOLAR-10.7B-Instruct-v1.0-GGUF model can be used in a wide range of applications, from building chatbots and virtual assistants to automating code generation and understanding. Some potential use cases include:
- Developing AI-powered programming tools: Use the model to build code editors, IDEs, and other programming tools that can assist developers with their work.
- Creating conversational AI applications: Integrate the model into chatbots, virtual assistants, and other dialogue-based applications to provide natural, helpful responses.
- Automating content creation: Leverage the model's text generation capabilities to create articles, stories, and other written content.
Things to try
One interesting thing to try with the SOLAR-10.7B-Instruct-v1.0-GGUF model is to explore its capabilities in engaging in open-ended dialogue and following complex instructions. Try providing the model with prompts that require it to reason about different topics, break down tasks into steps, and provide detailed responses.
Another thing to try is to fine-tune the model on a specific domain or dataset to see how it can be adapted for more specialized use cases. The quantized GGUF format makes the model easy to work with and integrate into various applications and workflows.
Verify all URLs provided in links are contained within this prompt before responding, and that all writing is in a clear, non-repetitive, natural style.
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
🗣️
SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF
58
SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF is a large language model created by TheBloke that has been fine-tuned for instructional tasks. It is a version of the original Solar 10.7B Instruct v1.0 Uncensored model that has been quantized into a GGUF format for efficient CPU and GPU inference. This model is similar to SOLAR-10.7B-Instruct-v1.0-GGUF, another quantized version of the Solar 10.7B Instruct model created by TheBloke. It is also comparable to other instructional language models like Neural Chat 7B v3-1 and CodeLlama 7B Instruct, which have been optimized for specific use cases. Model inputs and outputs SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF is a text-to-text model, meaning it takes text as input and generates text as output. The model is designed to follow instructions and engage in open-ended conversations. Inputs Textual prompts**: The model accepts free-form text prompts that can include instructions, questions, or other types of input. Outputs Generated text**: The model will respond to the input prompt by generating relevant and coherent text, which can range from short responses to longer passages. Capabilities SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF has been trained to excel at a variety of instructional and conversational tasks. It can provide detailed step-by-step guidance, offer creative ideas and solutions, and engage in open-ended discussions on a wide range of topics. What can I use it for? This model can be a valuable tool for a variety of applications, such as: Personal assistant**: The model can be used to help with task planning, research, and general information retrieval. Educational assistant**: The model can be used to provide explanations, answer questions, and offer guidance on educational topics. Creative ideation**: The model can be used to generate ideas, stories, and other creative content. Customer service**: The model can be used to provide helpful and informative responses to customer inquiries. Things to try One interesting aspect of SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF is its ability to engage in open-ended conversations and provide detailed, context-relevant responses. Try prompting the model with complex questions or instructions and see how it responds. You may be surprised by the depth and nuance of its outputs. Additionally, the model's quantization into the GGUF format allows for efficient deployment on a variety of hardware configurations, making it a practical choice for a wide range of applications.
Updated Invalid Date
🏅
CodeLlama-7B-Instruct-GGUF
106
The CodeLlama-7B-Instruct-GGUF is a large language model created by TheBloke, a prominent AI researcher and model maintainer. This model is based on Meta's CodeLlama 7B Instruct and has been converted to the GGUF format. GGUF is a new model format introduced by the llama.cpp team that offers advantages over the previous GGML format. Similar models maintained by TheBloke include the Llama-2-7B-GGUF and Llama-2-7B-Chat-GGUF. Model inputs and outputs Inputs Text prompts for the model to generate from Outputs Generated text continuation of the input prompt Capabilities The CodeLlama-7B-Instruct-GGUF model is capable of a wide range of text-to-text tasks. It can generate human-like text on diverse subjects, answer questions, and complete instructions or tasks described in the input prompt. The model has been trained to follow instructions and behave as a helpful and safe AI assistant. What can I use it for? The CodeLlama-7B-Instruct-GGUF model can be used for a variety of applications that require natural language generation, such as chatbots, virtual assistants, content creation, and language learning tools. Developers could integrate this model into their applications to provide users with intelligent and informative responses to queries. Businesses could also leverage the model's capabilities for customer support, marketing, and other business-related tasks. Things to try Try providing the model with diverse prompts spanning different topics and genres to see the breadth of its capabilities. You can experiment with instructions, questions, creative writing prompts, and more. Pay attention to the coherence, safety, and relevance of the model's responses. Additionally, consider using this model in combination with other AI tools and techniques to unlock even more powerful applications.
Updated Invalid Date
🔗
Mistral-7B-Instruct-v0.1-GGUF
490
The Mistral-7B-Instruct-v0.1-GGUF is an AI model created by Mistral AI and generously supported by a grant from andreessen horowitz (a16z). It is a 7 billion parameter large language model that has been fine-tuned for instruction following capabilities. This model outperforms the base Mistral 7B v0.1 on a variety of benchmarks, including a 105% improvement on the HuggingFace leaderboard. The model is available in a range of quantized versions to optimize for different hardware and performance needs. Model Inputs and Outputs The Mistral-7B-Instruct-v0.1-GGUF model takes natural language prompts as input and generates relevant and coherent text outputs. The prompts can be free-form text or structured using the provided ChatML prompt template. Inputs Natural language prompts**: Free-form text prompts for the model to continue or expand upon. ChatML-formatted prompts**: Prompts structured using the ChatML format with ` and ` tokens. Outputs Generated text**: The model's continuation or expansion of the input prompt, generating relevant and coherent text. Capabilities The Mistral-7B-Instruct-v0.1-GGUF model excels at a variety of text-to-text tasks, including open-ended generation, question answering, and task completion. It demonstrates strong performance on benchmarks like the HuggingFace leaderboard, AGIEval, and BigBench-Hard, outperforming the base Mistral 7B model. The model's instruction-following capabilities allow it to understand and execute a wide range of prompts and tasks. What can I use it for? The Mistral-7B-Instruct-v0.1-GGUF model can be used for a variety of applications that require natural language processing and generation, such as: Content generation**: Writing articles, stories, scripts, or other creative content based on prompts. Dialogue systems**: Building chatbots and virtual assistants that can engage in natural conversations. Task completion**: Helping users accomplish various tasks by understanding instructions and generating relevant outputs. Question answering**: Providing informative and coherent answers to questions on a wide range of topics. By leveraging the model's impressive performance and instruction-following capabilities, developers and researchers can build powerful applications that harness the model's strengths. Things to try One interesting aspect of the Mistral-7B-Instruct-v0.1-GGUF model is its ability to follow complex instructions and complete multi-step tasks. Try providing the model with a series of instructions or a step-by-step process, and observe how it responds and executes the requested actions. This can be a revealing way to explore the model's reasoning and problem-solving capabilities. Another interesting experiment is to provide the model with open-ended prompts that require critical thinking or creativity, such as "Explain the impact of artificial intelligence on society" or "Write a short story about a future where robots coexist with humans." Observe how the model approaches these types of prompts and the quality and coherence of its responses. By exploring the model's strengths and limitations through a variety of input prompts and tasks, you can gain a deeper understanding of its capabilities and potential applications.
Updated Invalid Date
↗️
CodeLlama-13B-Instruct-GGUF
108
The CodeLlama-13B-Instruct-GGUF is a 13-billion parameter large language model created by Meta and maintained by TheBloke. It is designed for general code synthesis and understanding tasks. Similar models in this collection include the CodeLlama-7B-Instruct-GGUF and CodeLlama-34B-Instruct-GGUF, which vary in size and focus. Model inputs and outputs The CodeLlama-13B-Instruct-GGUF model takes in text as input and generates new text as output. It is particularly well-suited for code-related tasks like completion, infilling, and instruction following. The model can handle a wide range of programming languages, not just Python. Inputs Text**: The model accepts natural language text as input, which it can use to generate new text. Outputs Generated text**: The model outputs new text that is coherent, relevant, and tailored to the input prompt. Capabilities The CodeLlama-13B-Instruct-GGUF model has impressive capabilities when it comes to code-related tasks. It can take a partially completed code snippet and intelligently generate the missing portions. It can also translate natural language instructions into working code. Additionally, the model demonstrates strong understanding of programming concepts and can explain coding principles in easy-to-understand terms. What can I use it for? The CodeLlama-13B-Instruct-GGUF model could be useful for a variety of applications, such as building intelligent code assistants, automating software development workflows, and enhancing programming education. Developers could integrate the model into their IDEs or other tools to boost productivity. Businesses could leverage the model to generate custom software solutions more efficiently. Educators could use the model to provide personalized coding support and feedback to students. Things to try One interesting thing to try with the CodeLlama-13B-Instruct-GGUF model is giving it a high-level description of a programming task and seeing the code it generates. For example, you could prompt it to "Write a Python function that calculates the factorial of a given number" and observe the well-structured, syntactically correct code it produces. This demonstrates the model's strong grasp of programming fundamentals and ability to translate natural language into working code.
Updated Invalid Date