Rombodawg
Models by this creator
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test_dataset_Codellama-3-8B
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test_dataset_Codellama-3-8B is an AI model trained by rombodawg on the Replete-AI/code-test-dataset. It is based on the unsloth/llama-3-8b-Instruct model and was trained using a combination of techniques including Qlora and Galore to enable training on Google Colab with under 15GB of VRAM. This model is similar to other Llama-3 8b models like the llama-3-8b-Instruct-bnb-4bit and llama-3-8b-bnb-4bit models, which are also 4-bit quantized versions of the Llama-3 8b model optimized for faster finetuning and lower memory usage. Model inputs and outputs The test_dataset_Codellama-3-8B model is a text-to-text AI model, meaning it takes text as input and generates text as output. Inputs Text prompts or instructions for the model to follow Outputs Generated text completing or responding to the input prompt Capabilities The test_dataset_Codellama-3-8B model is capable of natural language understanding and generation, allowing it to engage in tasks like answering questions, summarizing text, and generating written responses. However, as it was trained on a relatively small dataset, its capabilities may be more limited compared to larger language models. What can I use it for? This model could be used for a variety of text-based tasks, such as: Answering questions and providing information on a range of topics Summarizing longer text passages Generating short-form written content like product descriptions or social media posts Providing code-related assistance, such as explaining programming concepts or generating sample code However, due to the small dataset it was trained on, it may not be suitable for more complex or specialized tasks. Users should carefully evaluate the model's performance on their specific use case before deployment. Things to try Some ideas for things to try with the test_dataset_Codellama-3-8B model include: Experimenting with different prompts and instructions to see how the model responds Evaluating the model's performance on a variety of text-based tasks, such as question answering or text summarization Comparing the model's outputs to other similar language models to understand its strengths and limitations Exploring ways to fine-tune or further optimize the model for specific use cases Remember to always thoroughly test and validate the model's performance before deploying it in any critical applications.
Updated 5/30/2024
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Llama-3-8B-Instruct-Coder
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The Llama-3-8B-Instruct-Coder model is an AI language model developed by Meta and uploaded by the Hugging Face user rombodawg. This model is based on the Llama-3 family of large language models and has been fine-tuned on the CodeFeedback dataset, making it specialized for coding tasks. It was trained using the Qalore method, a new training technique developed by rombodawg's colleague at Replete-AI that allows the model to be loaded on 14.5 GB of VRAM. This is a significant improvement compared to previous Llama models, which required more VRAM. The Replete-AI community, which rombodawg is a part of, is very supportive and welcoming, as described on their Discord server. Model inputs and outputs The Llama-3-8B-Instruct-Coder model is a text-to-text model, meaning it takes text as input and generates text as output. The model is particularly adept at understanding and generating code, thanks to its fine-tuning on the CodeFeedback dataset. Inputs Text**: The model can accept a variety of text-based inputs, such as natural language instructions, coding prompts, or existing code snippets. Outputs Text**: The model will generate text-based outputs, which can include code, explanations, or responses to the given input. Capabilities The Llama-3-8B-Instruct-Coder model excels at a variety of coding-related tasks, such as code completion, code generation, and code understanding. It can be used to help developers write and debug code, as well as to generate new code based on natural language descriptions. The model's capabilities have been further enhanced by the Qalore training method, which has improved its performance and efficiency. What can I use it for? The Llama-3-8B-Instruct-Coder model can be a valuable tool for developers, programmers, and anyone working with code. It can be used to automate repetitive coding tasks, generate boilerplate code, or even create entire applications based on high-level requirements. The model's ability to understand and generate code also makes it useful for educational purposes, such as helping students learn programming concepts or providing feedback on their code. Things to try One interesting thing to try with the Llama-3-8B-Instruct-Coder model is to provide it with a natural language description of a coding problem and see how it responds. You can then compare the generated code to your own solution or to the expected output, and use the model's feedback to improve your understanding of the problem and the programming concepts involved.
Updated 9/6/2024
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Everyone-Coder-4x7b-Base
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The Everyone-Coder-4x7b-Base is a new Mixtral-type model created by leveraging experts that were fine-tuned by the community. This is the first model in the EveryoneLLM series, which aims to be a replacement or alternative to Mixtral-8x7b that is more suitable for general and specific use, as well as easier to fine-tune. The model was created by merging several expert models, including UNA-TheBeagle-7b-v1, openchat-3.5-0106-function-calling, WizardMath-7B-V1.1, and dolphin-2.6-mistral-7b-dpo-laser. The EveryoneLLM series aims to directly compete with Mistral's models, as Mistral has been secretive about the "secret sauce" that makes their Mixtral-Instruct model effective. Model inputs and outputs The Everyone-Coder-4x7b-Base is a text-to-text model, meaning it takes text as input and generates text as output. The model is designed to be a coding-specific model, with the goal of assisting users with a variety of programming tasks, such as debugging code, rewriting functions, optimizing scripts, and implementing new features. Inputs Coding-related prompts**: The model is trained on a variety of coding-related prompts, such as "Help me debug this code" or "Rewrite this function in Python". General language prompts**: The model can also handle more general language prompts, such as "How do you" or "Explain the concept of". Outputs Code-related responses**: The model generates responses that assist with coding tasks, such as providing suggestions for debugging code, optimizing scripts, or implementing new features. Explanatory responses**: The model can also generate responses that explain concepts or provide overviews on various topics. Capabilities The Everyone-Coder-4x7b-Base model is designed to be a versatile coding assistant, capable of handling a wide range of programming-related tasks. The model's strength lies in its ability to draw upon the expertise of the various models that were merged to create it, allowing it to provide high-quality, contextual responses to coding-related prompts. What can I use it for? The Everyone-Coder-4x7b-Base model can be a valuable tool for developers and programmers who need assistance with their coding tasks. Some potential use cases include: Code debugging and optimization**: The model can help identify and fix issues in code, as well as suggest ways to optimize existing scripts and applications. Feature implementation**: The model can provide guidance and suggestions for implementing new features or functionalities in a project. Code generation and rewriting**: The model can generate or rewrite code snippets based on high-level descriptions or requirements. Conceptual understanding**: The model can help explain programming concepts, algorithms, and best practices to users. Things to try One interesting aspect of the Everyone-Coder-4x7b-Base model is its ability to leverage the expertise of the various models that were merged to create it. Developers and researchers may want to experiment with prompts that target specific areas of expertise, such as math-focused prompts or prompts related to certain programming languages or frameworks. By exploring the model's capabilities across a range of coding-related tasks, users can gain a better understanding of its strengths and limitations, and how it can be most effectively utilized.
Updated 9/6/2024