gemma-2-2b-it-abliterated-GGUF
Maintainer: bartowski
46
💬
| 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 gemma-2-2b-it-abliterated-GGUF is a large language model created by maintainer bartowski. It is a quantized version of the original gemma-2-2b-it-abliterated model, optimized for smaller file size and faster inference using the llama.cpp library. The model has been quantized using various techniques to offer a range of quality and file size tradeoffs, from extremely high quality 8-bit quantized versions to more compressed 4-bit and 2-bit models with reduced performance.
Similar models include the gemma-2-9b-it-GGUF, Gemma-2-9B-It-SPPO-Iter3-GGUF, Llama-3-ChatQA-1.5-8B-GGUF, and Codestral-22B-v0.1-GGUF, all of which provide quantized versions of large language models optimized for various use cases and hardware constraints.
Model inputs and outputs
Inputs
- Prompt: The input text prompt to generate a response.
Outputs
- Generated text: The model's generated response to the input prompt.
Capabilities
The gemma-2-2b-it-abliterated-GGUF model is a powerful text generation model capable of a wide range of tasks, from open-ended conversation to creative writing and task-oriented dialogue. Its large size and broad training data allow it to display impressive natural language understanding and generation abilities.
What can I use it for?
The gemma-2-2b-it-abliterated-GGUF model can be used for a variety of applications, such as:
- Chatbots and virtual assistants: The model's conversational abilities make it well-suited for building engaging chatbots and virtual assistants.
- Content generation: The model can be used to generate various types of content, such as articles, stories, and even code.
- Text summarization: The model can be used to summarize long pieces of text into concise, informative summaries.
- Text translation: While not specifically trained for translation, the model's strong language understanding capabilities may enable it to perform basic translation tasks.
Things to try
One interesting aspect of the gemma-2-2b-it-abliterated-GGUF model is the variety of quantized versions available, each offering a different balance of file size and performance. Experimenting with these different quantized models can provide valuable insights into the tradeoffs between model size, inference speed, and overall quality. Additionally, comparing the performance of the gemma-2-2b-it-abliterated-GGUF model to the similar models mentioned earlier can help users determine the most suitable model for their specific hardware and use case requirements.
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
🔎
gemma-2-9b-it-GGUF
138
The gemma-2-9b-it-GGUF model is a quantized version of the Google/gemma-2-9b-it model, created by the maintainer bartowski. Similar models include the Codestral-22B-v0.1-GGUF, Meta-Llama-3-8B-Instruct-GGUF, LLaMA3-iterative-DPO-final-GGUF, and Llama-3-Lumimaid-8B-v0.1-OAS-GGUF-IQ-Imatrix. These models use the llama.cpp library for quantization, with various dataset and hyperparameter choices. Model inputs and outputs The gemma-2-9b-it-GGUF model is a text-to-text AI model, taking a user prompt as input and generating a corresponding text response. Inputs User prompt**: The text prompt provided by the user to the model. Outputs Generated text**: The text response generated by the model based on the user prompt. Capabilities The gemma-2-9b-it-GGUF model has been quantized to various file sizes, allowing users to choose a version that fits their hardware and performance requirements. The model is capable of generating high-quality, coherent text responses on a wide range of topics. It can be used for tasks such as language generation, text summarization, and question answering. What can I use it for? The gemma-2-9b-it-GGUF model can be used in a variety of applications, such as chatbots, content generation, and language-based assistants. For example, you could use the model to build a virtual assistant that can engage in natural conversations, or to generate summaries of long-form text. The maintainer has also provided quantized versions of other large language models, such as the Codestral-22B-v0.1-GGUF and Meta-Llama-3-8B-Instruct-GGUF, which may be suitable for different use cases or hardware constraints. Things to try One interesting thing to try with the gemma-2-9b-it-GGUF model is to experiment with the different quantization levels and their impact on performance and quality. The maintainer has provided a range of options, from the high-quality Q8_0 version to the more compact Q2_K and IQ2 variants. By testing these different versions, you can find the best balance between model size, inference speed, and output quality for your specific use case and hardware.
Updated Invalid Date
🤖
gemma-2-27b-it-GGUF
102
The gemma-2-27b-it-GGUF model is a quantized version of the original gemma-2-27b-it model, created by maintainer bartowski. Similar quantized models like gemma-2-9b-it-GGUF, LLaMA3-iterative-DPO-final-GGUF, Codestral-22B-v0.1-GGUF, and Meta-Llama-3-8B-Instruct-GGUF are also available from the same maintainer. Model inputs and outputs The gemma-2-27b-it-GGUF model is a text-to-text model, taking in a prompt as input and generating a text response as output. The model does not support a system prompt. Inputs Prompt**: The input text that the model will use to generate a response. Outputs Text response**: The model's generated output text, based on the input prompt. Capabilities The gemma-2-27b-it-GGUF model can be used for a variety of text generation tasks, such as language modeling, summarization, translation, and more. It has been quantized using llama.cpp to provide a range of options for file size and performance tradeoffs, allowing users to select the version that best fits their hardware and use case. What can I use it for? With its broad capabilities, the gemma-2-27b-it-GGUF model can be used for a wide range of applications, such as: Content Generation**: The model can be used to generate articles, stories, product descriptions, and other types of text content. Chatbots and Conversational Agents**: The model can be used to power the language understanding and response generation components of chatbots and virtual assistants. Summarization**: The model can be used to summarize long-form text, such as news articles or research papers. Translation**: The model can be used to translate text between different languages. Things to try One interesting aspect of the gemma-2-27b-it-GGUF model is the range of quantized versions available, allowing users to find the right balance between file size and performance for their specific needs. Users can experiment with the different quantization levels to see how they impact the model's output quality and speed, and choose the version that works best for their use case. Another interesting thing to try is using the model for tasks beyond just text generation, such as text classification or text-based reasoning. The model's broad language understanding capabilities may make it useful for a variety of NLP applications.
Updated Invalid Date
💬
Gemma-2-9B-It-SPPO-Iter3-GGUF
49
The Gemma-2-9B-It-SPPO-Iter3-GGUF is a large language model created by bartowski that has been quantized using llama.cpp. It is based on the original Gemma-2-9B-It-SPPO-Iter3 model. The model has been quantized to various levels of precision, ranging from full 32-bit floating-point weights to more compressed 4-bit and 2-bit quantized versions. This allows users to choose a model size that fits their hardware constraints while balancing performance. Similar quantized models include gemma-2-9b-it-GGUF and Phi-3-medium-128k-instruct-GGUF. Model inputs and outputs The Gemma-2-9B-It-SPPO-Iter3-GGUF model is a text-to-text model, meaning it takes text as input and generates text as output. Inputs Text prompt**: The text prompt provided to the model to generate a response. Outputs Generated text**: The model's response to the input text prompt. Capabilities The Gemma-2-9B-It-SPPO-Iter3-GGUF model is a capable language model that can be used for a variety of text generation tasks, such as content creation, summarization, translation, and more. It has been trained on a large corpus of text data and can generate coherent and contextually relevant responses. What can I use it for? The Gemma-2-9B-It-SPPO-Iter3-GGUF model can be used for a variety of applications, such as: Content creation**: Generate draft articles, stories, or other text-based content to jumpstart the creative process. Summarization**: Condense long passages of text into concise summaries. Translation**: Translate text between different languages. Chatbots**: Build conversational AI assistants to interact with users. Code generation**: Generate code snippets or complete programs based on natural language prompts. The model's quantized versions can be particularly useful for deploying the model on resource-constrained devices or in low-latency applications. Things to try One interesting aspect of the Gemma-2-9B-It-SPPO-Iter3-GGUF model is its ability to generate text with different levels of quality and file size by using the various quantized versions. Users can experiment with the different quantization levels to find the best balance of performance and file size for their specific use case. Additionally, the model's text generation capabilities can be further fine-tuned or adapted for specific domains or applications to enhance its usefulness.
Updated Invalid Date
🔍
Llama-3-ChatQA-1.5-8B-GGUF
42
The Llama-3-ChatQA-1.5-8B-GGUF model is a quantized version of the Llama-3-ChatQA-1.5-8B model, created by bartowski using the llama.cpp library. It is similar to other large language models like the Meta-Llama-3-8B-Instruct-GGUF and LLaMA3-iterative-DPO-final-GGUF models, which have also been quantized for reduced file size and improved performance. Model inputs and outputs The Llama-3-ChatQA-1.5-8B-GGUF model is a text-to-text model, meaning it takes text as input and generates text as output. The input can be a question, prompt, or any other type of text, and the output will be the model's response. Inputs Text**: The input text, which can be a question, prompt, or any other type of text. Outputs Text**: The model's response, which is generated based on the input text. Capabilities The Llama-3-ChatQA-1.5-8B-GGUF model is capable of engaging in open-ended conversations, answering questions, and generating text on a wide range of topics. It can be used for tasks such as chatbots, question-answering systems, and creative writing assistants. What can I use it for? The Llama-3-ChatQA-1.5-8B-GGUF model can be used for a variety of applications, such as: Chatbots**: The model can be used to build conversational AI assistants that can engage in natural language interactions. Question-Answering Systems**: The model can be used to create systems that can answer questions on a wide range of topics. Creative Writing Assistants**: The model can be used to generate text for creative writing tasks, such as story writing or poetry generation. Things to try One interesting thing to try with the Llama-3-ChatQA-1.5-8B-GGUF model is to explore the different quantization levels available and see how they affect the model's performance and output quality. The maintainer has provided a range of quantized versions with varying file sizes and quality levels, so you can experiment to find the right balance for your specific use case. Another thing to try is to fine-tune the model on a specific dataset or task, which can help it perform better on that task compared to the default pre-trained model. This could involve tasks like sentiment analysis, summarization, or task-oriented dialogue.
Updated Invalid Date