Unsloth
Models by this creator
🛠️
llama-3-8b-bnb-4bit
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.
Updated 5/28/2024
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llama-3-8b-Instruct-bnb-4bit
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.
Updated 5/28/2024
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Meta-Llama-3.1-8B-bnb-4bit
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The Meta-Llama-3.1-8B-bnb-4bit model is part of the Meta Llama 3.1 collection of multilingual large language models developed by Meta. This 8B parameter model is optimized for multilingual dialogue use cases and outperforms many open source and closed chat models on common industry benchmarks. It uses an auto-regressive transformer architecture and is trained on a mix of publicly available online data. The model supports text input and output in multiple languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Similar models in the Llama 3.1 family include the Meta-Llama-3.1-70B and Meta-Llama-3.1-405B which offer larger model sizes for more demanding applications. Other related models include the llama-3-8b from Unsloth which provides a finetuned version of the original Llama 3 8B model. Model inputs and outputs Inputs Multilingual Text**: The model accepts text input in multiple languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Multilingual Code**: The model can also accept code snippets in various programming languages. Outputs Multilingual Text**: The model generates text output in the same supported languages as the inputs. Multilingual Code**: The model can generate code outputs in various programming languages. Capabilities The Meta-Llama-3.1-8B-bnb-4bit model is particularly well-suited for multilingual dialogue and conversational tasks, outperforming many open source and closed chat models. It can engage in natural discussions, answer questions, and complete a variety of text generation tasks across different languages. The model also demonstrates strong capabilities in areas like reading comprehension, knowledge reasoning, and code generation. What can I use it for? This model could be used to power multilingual chatbots, virtual assistants, and other conversational AI applications. It could also be fine-tuned for specialized tasks like language translation, text summarization, or creative writing. Developers could leverage the model's outputs to generate synthetic data or distill knowledge into smaller models. The Llama Impact Grants program from Meta also highlights compelling applications of Llama models for societal benefit. Things to try One interesting aspect of this model is its ability to handle code generation in multiple programming languages, in addition to natural language tasks. Developers could experiment with using the model to assist with coding projects, generating test cases, or even drafting technical documentation. The model's multilingual capabilities also open up possibilities for cross-cultural communication and international collaboration.
Updated 9/18/2024
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llama-3-8b-Instruct
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llama-3-8b-Instruct is a large language model finetuned by Unsloth, a Hugging Face creator. It is based on the Llama-3 8B model and has been optimized for increased performance and reduced memory usage. Unsloth has developed notebooks that allow you to finetune the model 2-5x faster with 70% less memory, making it more accessible for a wider range of users and applications. Model inputs and outputs llama-3-8b-Instruct is a text-to-text model, capable of processing and generating natural language. It can be used for a variety of tasks, such as language modeling, text generation, and conversational AI. Inputs Natural language text Outputs Natural language text Capabilities The llama-3-8b-Instruct model has been finetuned to improve its performance and efficiency. Unsloth's notebooks allow you to finetune the model on your own dataset, resulting in a 2-5x speed increase and 70% reduction in memory usage compared to the original Llama-3 8B model. What can I use it for? The llama-3-8b-Instruct model can be used for a wide range of natural language processing tasks, such as text generation, language modeling, and conversational AI. Unsloth's finetuning process makes the model more accessible for a wider range of users and applications, as it can be deployed on less powerful hardware. Things to try You can use the provided Colab notebooks to finetune the llama-3-8b-Instruct model on your own dataset, which can then be exported and used in your own projects. Unsloth's optimization techniques allow for faster finetuning and more efficient model deployment, making it a versatile tool for natural language processing tasks.
Updated 7/18/2024
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llama-3-8b-Instruct
55
llama-3-8b-Instruct is a large language model finetuned by Unsloth, a Hugging Face creator. It is based on the Llama-3 8B model and has been optimized for increased performance and reduced memory usage. Unsloth has developed notebooks that allow you to finetune the model 2-5x faster with 70% less memory, making it more accessible for a wider range of users and applications. Model inputs and outputs llama-3-8b-Instruct is a text-to-text model, capable of processing and generating natural language. It can be used for a variety of tasks, such as language modeling, text generation, and conversational AI. Inputs Natural language text Outputs Natural language text Capabilities The llama-3-8b-Instruct model has been finetuned to improve its performance and efficiency. Unsloth's notebooks allow you to finetune the model on your own dataset, resulting in a 2-5x speed increase and 70% reduction in memory usage compared to the original Llama-3 8B model. What can I use it for? The llama-3-8b-Instruct model can be used for a wide range of natural language processing tasks, such as text generation, language modeling, and conversational AI. Unsloth's finetuning process makes the model more accessible for a wider range of users and applications, as it can be deployed on less powerful hardware. Things to try You can use the provided Colab notebooks to finetune the llama-3-8b-Instruct model on your own dataset, which can then be exported and used in your own projects. Unsloth's optimization techniques allow for faster finetuning and more efficient model deployment, making it a versatile tool for natural language processing tasks.
Updated 7/18/2024
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llama-3-8b
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The llama-3-8b is a large language model developed by Meta AI and finetuned by Unsloth. It is part of the Llama family of models, which also includes similar models like llama-3-8b-Instruct, llama-3-8b-bnb-4bit, and llama-3-8b-Instruct-bnb-4bit. Unsloth has provided notebooks to finetune these models 2-5x faster with 70% less memory usage. Model inputs and outputs The llama-3-8b model is a text-to-text transformer that can handle a wide variety of natural language tasks. It takes in text as input and generates text as output. Inputs Natural language text prompts Outputs Coherent, contextual text responses Capabilities The llama-3-8b model has been shown to excel at tasks like language generation, question answering, summarization, and more. It can be used to create engaging stories, provide detailed explanations, and assist with a variety of writing tasks. What can I use it for? The llama-3-8b model can be a powerful tool for a range of applications, from content creation to customer service chatbots. Its robust natural language understanding and generation capabilities make it well-suited for tasks like: Generating engaging blog posts, product descriptions, or creative writing Answering customer queries and providing personalized assistance Summarizing long-form content into concise overviews Translating text between languages Providing expert advice and information on a wide array of topics Things to try One interesting aspect of the llama-3-8b model is its ability to adapt to different styles and tones. By fine-tuning the model on domain-specific data, you can customize it to excel at specialized tasks like legal writing, technical documentation, or even poetry composition. The model's flexibility makes it a versatile tool for a variety of use cases.
Updated 9/6/2024
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llama-3-70b-bnb-4bit
44
The llama-3-70b-bnb-4bit model is a powerful language model developed by Unsloth. It is based on the Llama 3 architecture and has been optimized for faster finetuning and lower memory usage. The model is quantized to 4-bit precision using the bitsandbytes library, allowing it to achieve up to 70% less memory consumption compared to the original 8-bit version. Similar models provided by Unsloth include the llama-3-70b-Instruct-bnb-4bit, llama-3-8b, llama-3-8b-Instruct, llama-3-8b-Instruct-bnb-4bit, and llama-3-8b-bnb-4bit. These models offer various configurations and optimizations to suit different needs and hardware constraints. Model inputs and outputs Inputs Text**: The llama-3-70b-bnb-4bit model accepts natural language text as input, which can include prompts, questions, or instructions. Outputs Text**: The model generates coherent and contextually relevant text as output, which can be used for a variety of language tasks such as: Text completion Question answering Summarization Dialogue generation Capabilities The llama-3-70b-bnb-4bit model is capable of understanding and generating human-like text across a wide range of topics and domains. It can be used for tasks such as summarizing long documents, answering complex questions, and engaging in open-ended conversations. The model's performance is further enhanced by the 4-bit quantization, which allows for faster inference and lower memory usage without significantly compromising quality. What can I use it for? The llama-3-70b-bnb-4bit model can be employed in a variety of applications, such as: Content generation**: Generating high-quality text for articles, blog posts, product descriptions, or creative writing. Chatbots and virtual assistants**: Building conversational AI agents that can engage in natural dialogue and assist users with a wide range of tasks. Question answering**: Deploying the model as a knowledge base to provide accurate and informative answers to user queries. Summarization**: Condensing long-form text, such as reports or research papers, into concise and meaningful summaries. The model's efficiency and versatility make it a valuable tool for developers, researchers, and businesses looking to implement advanced language AI capabilities. Things to try One interesting aspect of the llama-3-70b-bnb-4bit model is its ability to handle open-ended prompts and engage in creative tasks. Try providing the model with diverse writing prompts, such as short story ideas or thought-provoking questions, and observe how it generates unique and imaginative responses. Additionally, you can experiment with fine-tuning the model on your own dataset to adapt it to specific domains or use cases.
Updated 9/6/2024
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llama-3-70b-Instruct-bnb-4bit
41
The llama-3-70b-Instruct-bnb-4bit model is a version of the Llama-3 language model that has been finetuned and quantized to 4-bit precision using the bitsandbytes library. This model was created by unsloth, who has developed a series of optimized Llama-based models that run significantly faster and use less memory compared to the original versions. The llama-3-70b-Instruct-bnb-4bit model is designed for text-to-text tasks and can be efficiently finetuned on a variety of datasets. Model inputs and outputs The llama-3-70b-Instruct-bnb-4bit model takes natural language text as input and generates natural language text as output. It can be used for a wide range of language tasks such as text generation, question answering, and language translation. Inputs Natural language text Outputs Natural language text Capabilities The llama-3-70b-Instruct-bnb-4bit model is capable of generating human-like text on a variety of topics. It can be used for tasks like creative writing, summarization, and dialogue generation. Due to its efficient design, the model can be finetuned quickly and run on modest hardware. What can I use it for? The llama-3-70b-Instruct-bnb-4bit model can be used for a variety of natural language processing tasks, such as: Content Generation**: Use the model to generate articles, stories, or other long-form text content. Summarization**: Summarize long documents or conversations into concise summaries. Question Answering**: Fine-tune the model on a knowledge base to answer questions on a wide range of topics. Dialogue Systems**: Use the model to power chatbots or virtual assistants that can engage in natural conversations. Things to try One interesting aspect of the llama-3-70b-Instruct-bnb-4bit model is its ability to be efficiently finetuned on custom datasets. This makes it well-suited for tasks that require domain-specific knowledge, such as scientific writing, legal analysis, or financial reporting. By finetuning the model on a relevant dataset, you can imbue it with specialized expertise and capabilities. Another area to explore is the model's potential for multilingual applications. While the base Llama-3 model was trained on a diverse set of languages, the finetuned llama-3-70b-Instruct-bnb-4bit variant may exhibit particularly strong performance on certain language pairs or domains. Experimenting with cross-lingual fine-tuning and evaluation could yield interesting insights.
Updated 9/6/2024
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Phi-3-mini-4k-instruct
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The Phi-3-mini-4k-instruct model is a lightweight, state-of-the-art open model developed by unsloth that builds upon datasets used for Phi-2, with a focus on high-quality, reasoning-dense data. The model is part of the Phi-3 family and comes in two variants: 4K and 128K, which refers to the maximum context length (in tokens) it can support. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization, to ensure precise instruction adherence and robust safety measures. The Phi-3-mini-4k-instruct model is similar to other models in the Phi-3 family, such as the llama-3-8b-instruct, llama-3-8b-bnb-4bit, and Phi-3-mini-4k-instruct-onnx models, all of which are optimized for improved performance and efficiency. Model inputs and outputs Inputs Text prompt**: The model takes in a text prompt, which can be a natural language query, instruction, or any other text input. Outputs Text response**: The model generates a relevant text response based on the input prompt. Capabilities The Phi-3-mini-4k-instruct model is a powerful natural language processing model that can be used for a variety of tasks, such as text generation, question answering, and language understanding. It is particularly well-suited for tasks that require precise instruction adherence and reasoning, as it has been optimized for these capabilities. What can I use it for? The Phi-3-mini-4k-instruct model can be used for a wide range of applications, such as chatbots, virtual assistants, language translation, and content generation. Its compact size and efficient performance make it a great choice for deployment on a variety of platforms, from mobile devices to cloud-based services. Things to try One interesting aspect of the Phi-3-mini-4k-instruct model is its ability to generate high-quality, coherent text while using significantly less memory and processing power than larger language models. You could try fine-tuning the model on your own dataset to see how it performs on specific tasks, or experiment with different prompting techniques to unlock its full potential.
Updated 9/6/2024