Unsloth

Models by this creator

🛠️

llama-3-8b-bnb-4bit

unsloth

Total Score

112

The llama-3-8b-bnb-4bit model is a version of the Meta Llama 3 language model that has been quantized to 4-bit precision using the bitsandbytes library. This model was created by the maintainer unsloth and is designed to provide faster finetuning and lower memory usage compared to the original Llama 3 model. The maintainer has also created quantized 4-bit versions of other large language models like Gemma 7b, Mistral 7b, Llama-2 7b, and TinyLlama, all of which can be finetuned 2-5x faster with 43-74% less memory usage. Model inputs and outputs Inputs Natural language text prompts Outputs Natural language text continuations and completions Capabilities The llama-3-8b-bnb-4bit model can be used for a variety of text generation tasks, such as language modeling, text summarization, and question answering. The maintainer has provided examples of using this model to finetune on custom datasets and export the resulting models for use in other applications. What can I use it for? The llama-3-8b-bnb-4bit model can be a useful starting point for a wide range of natural language processing projects that require a large language model with reduced memory and faster finetuning times. For example, you could use this model to build chatbots, content generation tools, or other applications that rely on text-based AI. The maintainer has also provided a Colab notebook to help get you started with finetuning the model. Things to try One interesting aspect of the llama-3-8b-bnb-4bit model is its ability to be finetuned quickly and efficiently. This could make it a good choice for quickly iterating on new ideas or testing different approaches to a problem. Additionally, the reduced memory usage of the 4-bit quantized model could allow you to run it on less powerful hardware, opening up more opportunities to experiment and deploy your models.

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Updated 5/28/2024

👀

llama-3-8b-Instruct-bnb-4bit

unsloth

Total Score

79

The llama-3-8b-Instruct-bnb-4bit model is a 4-bit quantized version of the Llama-3 8B model, created by the maintainer unsloth. This model is finetuned using the bitsandbytes library, allowing for faster inference with 70% less memory usage compared to the original Llama-3 8B model. The maintainer has also provided finetuned models for other large language models like Gemma 7B, Mistral 7B, and Llama-2 7B, all of which see similar performance and memory usage improvements. Similar models include the Llama2-7b-chat-hf_1bitgs8_hqq model, which is a 1-bit quantized version of the Llama2-7B-chat model using a low-rank adapter, and the 2-bit-LLMs collection, which contains 2-bit quantized versions of various large language models. Model inputs and outputs Inputs Text prompts**: The llama-3-8b-Instruct-bnb-4bit model accepts natural language text prompts as input, which it then uses to generate relevant text outputs. Outputs Text completions**: The model outputs coherent and contextually appropriate text continuations based on the provided input prompts. Capabilities The llama-3-8b-Instruct-bnb-4bit model has been finetuned for instruction-following and can perform a wide variety of language tasks, such as question answering, summarization, and task completion. Due to its reduced memory footprint, the model can be deployed on lower-resource hardware while still maintaining good performance. What can I use it for? The llama-3-8b-Instruct-bnb-4bit model can be used for a variety of natural language processing applications, such as building chatbots, virtual assistants, and content generation tools. The maintainer has provided Colab notebooks to help users get started with finetuning the model on their own datasets, allowing for the creation of customized language models for specific use cases. Things to try One interesting aspect of the llama-3-8b-Instruct-bnb-4bit model is its ability to be finetuned quickly and efficiently, thanks to the 4-bit quantization and the use of the bitsandbytes library. Users can experiment with finetuning the model on their own datasets to create specialized language models tailored to their needs, while still benefiting from the performance and memory usage improvements compared to the original Llama-3 8B model.

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Updated 5/28/2024