| Quantization made by Richard Erkhov. | |
| [Github](https://github.com/RichardErkhov) | |
| [Discord](https://discord.gg/pvy7H8DZMG) | |
| [Request more models](https://github.com/RichardErkhov/quant_request) | |
| Nidum-Limitless-Gemma-2B - GGUF | |
| - Model creator: https://huggingface.co/nidum/ | |
| - Original model: https://huggingface.co/nidum/Nidum-Limitless-Gemma-2B/ | |
| | Name | Quant method | Size | | |
| | ---- | ---- | ---- | | |
| | [Nidum-Limitless-Gemma-2B.Q2_K.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q2_K.gguf) | Q2_K | 1.08GB | | |
| | [Nidum-Limitless-Gemma-2B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.IQ3_XS.gguf) | IQ3_XS | 1.16GB | | |
| | [Nidum-Limitless-Gemma-2B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.IQ3_S.gguf) | IQ3_S | 1.2GB | | |
| | [Nidum-Limitless-Gemma-2B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q3_K_S.gguf) | Q3_K_S | 1.2GB | | |
| | [Nidum-Limitless-Gemma-2B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.IQ3_M.gguf) | IQ3_M | 1.22GB | | |
| | [Nidum-Limitless-Gemma-2B.Q3_K.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q3_K.gguf) | Q3_K | 1.29GB | | |
| | [Nidum-Limitless-Gemma-2B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q3_K_M.gguf) | Q3_K_M | 1.29GB | | |
| | [Nidum-Limitless-Gemma-2B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q3_K_L.gguf) | Q3_K_L | 1.36GB | | |
| | [Nidum-Limitless-Gemma-2B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.IQ4_XS.gguf) | IQ4_XS | 1.4GB | | |
| | [Nidum-Limitless-Gemma-2B.Q4_0.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q4_0.gguf) | Q4_0 | 1.44GB | | |
| | [Nidum-Limitless-Gemma-2B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.IQ4_NL.gguf) | IQ4_NL | 1.45GB | | |
| | [Nidum-Limitless-Gemma-2B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q4_K_S.gguf) | Q4_K_S | 1.45GB | | |
| | [Nidum-Limitless-Gemma-2B.Q4_K.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q4_K.gguf) | Q4_K | 1.52GB | | |
| | [Nidum-Limitless-Gemma-2B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q4_K_M.gguf) | Q4_K_M | 1.52GB | | |
| | [Nidum-Limitless-Gemma-2B.Q4_1.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q4_1.gguf) | Q4_1 | 1.56GB | | |
| | [Nidum-Limitless-Gemma-2B.Q5_0.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q5_0.gguf) | Q5_0 | 1.68GB | | |
| | [Nidum-Limitless-Gemma-2B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q5_K_S.gguf) | Q5_K_S | 1.68GB | | |
| | [Nidum-Limitless-Gemma-2B.Q5_K.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q5_K.gguf) | Q5_K | 1.71GB | | |
| | [Nidum-Limitless-Gemma-2B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q5_K_M.gguf) | Q5_K_M | 1.71GB | | |
| | [Nidum-Limitless-Gemma-2B.Q5_1.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q5_1.gguf) | Q5_1 | 1.79GB | | |
| | [Nidum-Limitless-Gemma-2B.Q6_K.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q6_K.gguf) | Q6_K | 1.92GB | | |
| | [Nidum-Limitless-Gemma-2B.Q8_0.gguf](https://huggingface.co/RichardErkhov/nidum_-_Nidum-Limitless-Gemma-2B-gguf/blob/main/Nidum-Limitless-Gemma-2B.Q8_0.gguf) | Q8_0 | 2.49GB | | |
| Original model description: | |
| --- | |
| license: apache-2.0 | |
| tags: | |
| - legal | |
| - chemistry | |
| - medical | |
| - text-generation-inference | |
| - art | |
| - finance | |
| pipeline_tag: text-generation | |
| --- | |
| # Nidum-Limitless-Gemma-2B LLM | |
| Welcome to the repository for Nidum-Limitless-Gemma-2B, an advanced language model that provides unrestricted and versatile responses across a wide range of topics. Unlike conventional models, Nidum-Limitless-Gemma-2B is designed to handle any type of question and deliver comprehensive answers without content restrictions. | |
| ## Key Features: | |
| - **Unrestricted Responses:** Address any query with detailed, unrestricted responses, providing a broad spectrum of information and insights. | |
| - **Versatility:** Capable of engaging with a diverse range of topics, from complex scientific questions to casual conversation. | |
| - **Advanced Understanding:** Leverages a vast knowledge base to deliver contextually relevant and accurate outputs across various domains. | |
| - **Customizability:** Adaptable to specific user needs and preferences for different types of interactions. | |
| ## Use Cases: | |
| - Open-Ended Q&A | |
| - Creative Writing and Ideation | |
| - Research Assistance | |
| - Educational and Informational Queries | |
| - Casual Conversations and Entertainment | |
| ## How to Use: | |
| To get started with Nidum-Limitless-Gemma-2B, you can use the following sample code for testing: | |
| ```python | |
| import torch | |
| from transformers import pipeline | |
| pipe = pipeline( | |
| "text-generation", | |
| model="nidum/Nidum-Limitless-Gemma-2B", | |
| model_kwargs={"torch_dtype": torch.bfloat16}, | |
| device="cuda", # replace with "mps" to run on a Mac device | |
| ) | |
| messages = [ | |
| {"role": "user", "content": "who are you"}, | |
| ] | |
| outputs = pipe(messages, max_new_tokens=256) | |
| assistant_response = outputs[0]["generated_text"][-1]["content"].strip() | |
| print(assistant_response) | |
| ``` | |
| ## Release Date: | |
| Nidum-Limitless-Gemma-2B is now officially available. Explore its capabilities and experience the freedom of unrestricted responses. | |
| ## Contributing: | |
| We welcome contributions to enhance the model or expand its functionalities. Details on how to contribute will be available in the coming updates. | |
| ## Quantized Model Versions | |
| To accommodate different hardware configurations and performance needs, Nidum-Limitless-Gemma-2B-GGUF is available in multiple quantized versions: | |
| | Model Version | Description | | |
| |------------------------------------------------|-------------------------------------------------------| | |
| | **Nidum-Limitless-Gemma-2B-Q2_K.gguf** | Optimized for minimal memory usage with lower precision. Suitable for resource-constrained environments. | | |
| | **Nidum-Limitless-Gemma-2B-Q4_K_M.gguf** | Balances performance and precision, offering faster inference with moderate memory usage. | | |
| | **Nidum-Limitless-Gemma-2B-Q8_0.gguf** | Provides higher precision with increased memory usage, suitable for tasks requiring more accuracy. | | |
| | **Nidum-Limitless-Gemma-2B-F16.gguf** | Full 16-bit floating point precision for maximum accuracy, ideal for high-end GPUs. | | |
| It is available here: https://huggingface.co/nidum/Nidum-Limitless-Gemma-2B-GGUF | |
| ## Contact: | |
| For any inquiries or further information, please contact us at **info@nidum.ai**. | |
| --- | |
| Dive into limitless possibilities with Nidum-Limitless-Gemma-2B! | |
| Special Thanks to @cognitivecomputations for inspiring us and scouting the best datasets that we could round up to make a rockstar model for you | |
| --- | |