wizardLM-7B-GGML
Maintainer: TheBloke
157
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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 wizardLM-7B-GGML model is a large language model developed by TheBloke, a prominent AI model creator. This model is part of the WizardLM family of models, which range in scale from 7 billion to 70 billion parameters. The wizardLM-7B-GGML model is available in a variety of quantized GGML formats, providing options for different performance and resource requirements.
Similar models from TheBloke include the Llama-2-7B-GGML and Llama-2-13B-GGML models, which are based on Meta's Llama 2 architecture and also available in quantized GGML formats.
Model inputs and outputs
Inputs
- Text: The
wizardLM-7B-GGMLmodel takes text input and generates text output.
Outputs
- Text: The model generates coherent, contextual text based on the input.
Capabilities
The wizardLM-7B-GGML model is a powerful language model capable of a wide range of natural language processing tasks, such as text generation, question answering, and language understanding. It can be used to create engaging dialogues, summarize text, and even generate creative content.
What can I use it for?
The wizardLM-7B-GGML model can be used for a variety of projects, including chatbots, content creation, and language learning applications. Its quantized GGML formats make it suitable for deployment on CPU and GPU systems, allowing for efficient inference on a range of hardware.
Things to try
One interesting aspect of the wizardLM-7B-GGML model is its ability to generate coherent and context-aware text. Try providing it with prompts that require reasoning, such as "Explain the economic impact of the recent policy changes in a way that a 10-year-old would understand." The model should be able to generate a clear and simplified explanation, demonstrating its language understanding and generation capabilities.
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 WizardLM-13B-V1.2-GGML model is a large language model created by WizardLM. It is a 13 billion parameter version of the WizardLM model that has been quantized to run on CPU and GPU hardware. This model is similar to other WizardLM and wizardLM-7B-GGML models, as they are all part of TheBloke's efforts to provide high-quality open-source language models. Model inputs and outputs The WizardLM-13B-V1.2-GGML model is a text-to-text model, meaning it takes natural language text as input and generates natural language text as output. The model can be used for a variety of tasks, such as language generation, question answering, and text summarization. Inputs Natural language text prompts Outputs Generated natural language text Capabilities The WizardLM-13B-V1.2-GGML model has been trained on a large corpus of text data, allowing it to generate coherent and contextually-relevant responses to a wide range of prompts. It has been designed to be helpful, informative, and engaging in its interactions. What can I use it for? The WizardLM-13B-V1.2-GGML model can be used for a variety of applications, such as: Content generation: The model can be used to generate articles, stories, or other types of text content. Chatbots and virtual assistants: The model can be used to power conversational interfaces, providing natural language responses to user queries. Question answering: The model can be used to answer a wide range of questions on various topics. Text summarization: The model can be used to generate concise summaries of longer pieces of text. Things to try One interesting thing to try with the WizardLM-13B-V1.2-GGML model is to explore its versatility by providing it with prompts across different domains, such as creative writing, technical instructions, or open-ended questions. This can help you understand the model's capabilities and limitations, and identify areas where it excels or struggles.
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WizardLM-7B-uncensored-GGML
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The WizardLM-7B-uncensored-GGML is a large language model created by Eric Hartford and maintained by TheBloke. It is an "uncensored" version of the WizardLM-7B model, meaning it has had certain alignment and moralizing responses removed from the training data. This is intended to create a more flexible model that allows the user to add their own alignment or other biases as needed, rather than having them baked into the model itself. The model is available in a variety of quantized GGML formats, which allow for efficient inference on both CPUs and GPUs using tools like llama.cpp and compatible UIs. TheBloke has also provided similar uncensored models at larger scales, including the WizardLM-13B-Uncensored-GGML and WizardLM-30B-Uncensored-GGML. Model inputs and outputs Inputs Prompt**: The input text that the model will use to generate a response. Outputs Generated text**: The model's response to the provided prompt, which can be of variable length. Capabilities The WizardLM-7B-uncensored-GGML model is a powerful language generation tool that can be used for a variety of tasks, such as writing stories, answering questions, or engaging in open-ended conversations. Its "uncensored" nature means it does not have the same built-in ethical constraints as some other language models, allowing for more flexible and wide-ranging outputs. What can I use it for? This model could be useful for creative writing projects, chatbots, virtual assistants, and other applications where an open-ended, versatile language generation model is needed. However, its uncensored nature also means it requires careful consideration and responsibility when using, as the outputs may contain content that some find objectionable. It's important to establish appropriate guardrails and moderation when deploying this model in real-world applications. Things to try One interesting aspect of the WizardLM-7B-uncensored-GGML model is its ability to generate highly detailed and imaginative stories. You could try providing it with a simple prompt about a fantasy world or character, and see what kind of elaborate narrative it comes up with. Another interesting experiment would be to fine-tune the model on a specific dataset or task, and observe how its outputs change compared to the base uncensored model.
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WizardLM-13B-Uncensored-GGML
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The WizardLM-13B-Uncensored-GGML is an AI model created by Eric Hartford and maintained by TheBloke. It is a 13-billion parameter language model based on the LLaMA architecture, trained on a subset of the dataset with responses containing alignment or moralizing removed. This aims to produce an uncensored model that can have alignment added separately, such as through a RLHF LoRA. Similar models maintained by TheBloke include the WizardLM-30B-Uncensored-GGML, the Wizard-Vicuna-7B-Uncensored-GGML, and the wizardLM-7B-GGML. Model inputs and outputs The WizardLM-13B-Uncensored-GGML model takes text prompts as input and generates coherent, context-appropriate text as output. The model can be used for a variety of natural language tasks, including content generation, question answering, and language translation. Inputs Text prompts**: The model takes natural language text prompts as input, which can be of varying lengths. Outputs Generated text**: The model outputs generated text that is coherent, context-appropriate, and grammatically correct. The length of the output can be specified. Capabilities The WizardLM-13B-Uncensored-GGML model is capable of generating high-quality, natural-sounding text on a wide range of topics. Due to its large size and training on a diverse dataset, the model can engage in open-ended conversation, answer questions, and even write creative fiction or poetry. What can I use it for? The WizardLM-13B-Uncensored-GGML model can be used for a variety of natural language processing tasks, such as content generation, summarization, translation, and question answering. It could be particularly useful for applications that require engaging, context-appropriate language, such as chatbots, writing assistants, and creative writing tools. Things to try One interesting aspect of the WizardLM-13B-Uncensored-GGML model is its lack of built-in alignment or censorship, which allows for more open-ended and potentially controversial outputs. Users could experiment with prompts that explore the model's limits and capabilities in this regard, while being mindful of the responsibility involved in publishing the generated content.
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WizardLM-30B-Uncensored-GGML
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The WizardLM-30B-Uncensored-GGML model is an expansive language model created by Eric Hartford and maintained by TheBloke. It is a 30 billion parameter model that has been trained on a large corpus of text without any censorship or alignment imposed. This model can be contrasted with the wizardLM-7B-GGML and Wizard-Vicuna-30B-Uncensored-GGML models, which are smaller or use a different training approach. Model inputs and outputs Inputs Text prompts**: The model accepts text-based prompts as input, which can be used to generate coherent and contextual responses. Outputs Text generation**: The primary output of the model is the generation of human-like text, with the ability to continue a conversation, generate stories, or provide informative responses to prompts. Capabilities The WizardLM-30B-Uncensored-GGML model has a wide range of capabilities due to its large size and diverse training data. It can engage in open-ended dialogue, answer questions, generate creative writing, and even tackle more specialized tasks like code generation or task planning. However, as an uncensored model, it lacks the alignment and safety precautions of some other language models, so users should exercise caution when deploying it. What can I use it for? This model could be useful for a variety of applications, such as building conversational AI assistants, generating creative content, or even accelerating the development of other AI models through fine-tuning or prompt engineering. However, given the uncensored nature of the model, it would need to be used with care and responsibility, especially in any public-facing or commercial applications. Things to try One interesting thing to try with this model is exploring its ability to engage in open-ended dialogue on a wide range of topics. You could prompt it with questions about current events, philosophical questions, or even requests for creative writing, and see the diverse and often surprising responses it generates. However, it's important to keep in mind the potential risks of an uncensored model and to monitor the outputs carefully.
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