Kaiokendev
Models by this creator
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SuperCOT-LoRA
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SuperCOT-LoRA is a Large Language Model (LLM) that has been fine-tuned using a variety of datasets to improve its ability to follow prompts for Langchain. It was developed by kaiokendev and builds upon the existing LLaMA model by infusing it with datasets focused on chain-of-thought, code explanations, instructions, and logical deductions. Similar models like Llama-3-8B-Instruct-262k from Gradient also aim to extend the context length and instructional capabilities of large language models. However, SuperCOT-LoRA is specifically tailored towards improving the model's ability to follow Langchain prompts through the incorporation of specialized datasets. Model inputs and outputs Inputs SuperCOT-LoRA accepts text input, similar to other autoregressive language models. Outputs The model generates text outputs, which can include responses to prompts, code explanations, and logical deductions. Capabilities SuperCOT-LoRA is designed to be particularly adept at following Langchain prompts and producing outputs that are well-suited for use within Langchain workflows. By incorporating datasets focused on chain-of-thought, code explanations, and logical reasoning, the model has been trained to provide more coherent and contextually-appropriate responses when working with Langchain. What can I use it for? The SuperCOT-LoRA model can be particularly useful for developers and researchers working on Langchain-based applications. Its specialized training allows it to generate outputs that are tailored for use within Langchain, making it a valuable tool for tasks such as: Building conversational AI assistants that can engage in multi-step logical reasoning Developing code generation and explanation tools that integrate seamlessly with Langchain Enhancing the capabilities of existing Langchain-powered applications with more advanced language understanding and generation Things to try One interesting aspect of SuperCOT-LoRA is its potential to improve the coherence and contextual awareness of Langchain-based applications. By leveraging the model's enhanced ability to follow prompts and maintain logical flow, developers could experiment with building more sophisticated question-answering systems, or task-oriented chatbots that can better understand and respond to user intents. Additionally, the model's training on code-related datasets could make it a useful tool for generating and explaining code snippets within Langchain-powered applications, potentially enhancing the developer experience for users.
Updated 5/28/2024
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superhot-13b-8k-no-rlhf-test
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The superhot-13b-8k-no-rlhf-test model, developed by kaiokendev, is a second prototype of the SuperHOT language model. It features a 13B parameter size and an increased context length of up to 8K tokens, without the use of Reinforcement Learning from Human Feedback (RLHF). This model builds upon the techniques described in kaiokendev's blog post to extend the context length beyond the typical 2K-4K range. Similar models include the Pygmalion-13B-SuperHOT-8K-GPTQ and the Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ, both of which incorporate the SuperHOT techniques to increase the context length. Model inputs and outputs Inputs Text prompts of up to 8192 tokens Outputs Continuation of the input text, with the model generating new text based on the provided context Capabilities The superhot-13b-8k-no-rlhf-test model is capable of generating text with an extended context length of up to 8192 tokens. This allows the model to maintain coherence and consistency over longer passages of text, making it suitable for tasks that require understanding and generating content over multiple paragraphs or pages. What can I use it for? The extended context length of this model makes it well-suited for applications that require generating long-form content, such as creative writing, article generation, or summarization. The lack of RLHF means the model may be less constrained in its outputs, potentially allowing for more diverse and experimental content generation. Things to try One key aspect to explore with this model is the impact of the extended context length on the generated text. You can experiment with prompts that span multiple paragraphs or pages to see how the model maintains coherence and consistency over longer passages. Additionally, you can try comparing the outputs of this model to those of models with more typical context lengths to understand the differences in the generated content.
Updated 5/28/2024