Deci
Models by this creator
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DeciCoder-1b
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DeciCoder-1b is a 1 billion parameter decoder-only code completion model developed by Deci. It was trained on the Python, Java, and Javascript subsets of the Starcoder Training Dataset. The model uses Grouped Query Attention and has a context window of 2048 tokens. It was trained using a Fill-in-the-Middle training objective. The DeciCoder-1b model can be compared to similar code generation models like starcoder2-15b, starcoder, starcoderbase, and stable-code-3b. These models share capabilities around code generation, completion, and understanding, though they differ in their specific architectures, training data, and performance characteristics. Model Inputs and Outputs The DeciCoder-1b model is a text-to-text model, taking in textual prompts as input and generating continuations or completions as output. Inputs Textual prompts related to code, such as function signatures, comments, or partial code snippets. Outputs Continuations or completions of the input code, generated in an auto-regressive manner. The model can generate single or multi-line code completions based on the provided context. Capabilities The DeciCoder-1b model is capable of generating coherent and context-appropriate code completions for common programming languages like Python, Java, and JavaScript. It can leverage the provided context to continue or complete a code snippet in a sensible way, though the generated code may not always be fully correct or optimal. What Can I Use it For? The DeciCoder-1b model can be a useful tool for developers working on code-related tasks. Some potential use cases include: Code completion and suggestion during programming to boost productivity Generating boilerplate code or code templates based on a high-level description Prototyping new features or algorithms by providing a starting prompt Exploring novel code ideas by iterating on generated outputs However, it's important to note that the generated code may not always be reliable or production-ready, and should be thoroughly tested and validated before deployment. Things to Try One interesting aspect of the DeciCoder-1b model is its ability to perform "fill-in-the-middle" generation. This allows you to provide a partial code snippet with placeholders, and have the model generate the missing middle portion. This can be a useful technique for exploring different ways to implement a specific logic or algorithm. Another interesting experiment would be to compare the performance of DeciCoder-1b to other similar models like starcoder2-15b or stable-code-3b on specific coding tasks or benchmarks. This could help you understand the relative strengths and weaknesses of the different models.
Updated 5/28/2024
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DeciLM-6b
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DeciLM-6b is a 5.7 billion parameter decoder-only text generation model developed by Deci. With a context window of 4096 tokens, the highly efficient model uses variable Grouped-Query Attention (GQA) to achieve an optimal balance between performance and computational efficiency. The model's architecture was generated using Deci's proprietary Neural Architecture Search-based technology, AutoNAC. DeciLM-6b outpaces pretrained models in its class, with a throughput that's up to 15 times that of LLaMA 2 7B. It was further fine-tuned using LoRA for instruction following on a subset of the OpenOrca dataset, creating DeciLM 6B-Instruct. Model inputs and outputs DeciLM-6b is a text generation model that takes text prompts as input and generates coherent, human-like text as output. The model can be used for a variety of text-based tasks, such as: Inputs Text prompts Context windows up to 4096 tokens Outputs Relevant, human-like text continuations Responses to instructions and queries Capabilities DeciLM-6b is capable of generating high-quality, informative text across a range of topics. It can effectively handle tasks like: Summarizing information Answering questions Generating creative stories and narratives Translating text between languages Providing informative and engaging responses to prompts The model's exceptional efficiency and throughput make it well-suited for applications that require fast, high-volume text generation. What can I use it for? DeciLM-6b is a versatile model that can be applied to a variety of commercial and research use cases, such as: Content generation for websites, marketing materials, and social media Chatbots and virtual assistants Summarization and information extraction Educational and training applications Research into large language models and their capabilities The model's open-source license and pre-trained weights make it easy to integrate into your own projects and applications. Things to try One interesting aspect of DeciLM-6b is its use of variable Grouped-Query Attention (GQA), which allows the model to balance performance and efficiency. You could experiment with how adjusting the number of key-value heads in the GQA layers affects the model's capabilities and performance. Additionally, the model's fine-tuning on the OpenOrca dataset for instruction following suggests that it may excel at tasks that require understanding and carrying out complex instructions. You could try providing the model with a variety of instruction-based prompts to see how it responds.
Updated 5/27/2024
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DeciLM-7B
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DeciLM-7B is a 7.04 billion parameter decoder-only text generation model developed by Deci. At the time of release, it is the top-performing 7B base language model on the Open LLM Leaderboard. DeciLM-7B uses an optimized transformer decoder architecture that includes variable Grouped-Query Attention (GQA) to achieve a superior balance between accuracy and computational efficiency. Deci's proprietary Neural Architecture Search technology, AutoNAC, was used to generate the model's architecture. Similar models include the DeciLM-6B and DeciCoder-1B, which are also developed by Deci and leverage architectural optimizations like GQA and ALiBi to achieve high performance. Model inputs and outputs Inputs Text prompt**: DeciLM-7B takes a text prompt as input and generates additional text based on that prompt. Outputs Generated text**: The model outputs generated text that continues or expands upon the provided prompt. Capabilities DeciLM-7B demonstrates strong performance on a variety of benchmarks, including the Open LLM Leaderboard, C-Eval, and Gaokao. It outperforms many other 7B-scale models in terms of accuracy and computational efficiency. The model's long sequence length (up to 8192 tokens) and ability to leverage variable Grouped-Query Attention make it well-suited for applications that require generating coherent, long-form text. What can I use it for? DeciLM-7B is intended for commercial and research use in English and can be fine-tuned for various tasks and languages. Some potential use cases include: Content generation**: The model can be used to generate articles, stories, or other long-form text content. Language modeling**: The model can be used as a base for further fine-tuning on specialized tasks or datasets. Code generation**: The model's ability to generate coherent text could potentially be leveraged for code completion or generation tasks. Things to try One interesting aspect of DeciLM-7B is its use of variable Grouped-Query Attention, which allows the model to balance accuracy and computational efficiency. Experimenting with different configurations of the GQA hyperparameters, such as the number of key-value heads, could yield insights into how this architectural choice impacts model performance. Additionally, the model's support for long sequence lengths (up to 8192 tokens) opens up opportunities to explore generation tasks that require maintaining coherence over extended text. Prompting the model with a paragraph-length input and observing the quality of the generated continuation could be a valuable exercise.
Updated 5/27/2024
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DeciDiffusion-v1-0
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DeciDiffusion-v1-0 is an 820 million parameter text-to-image latent diffusion model developed by Deci. It was trained on the LAION-v2 dataset and fine-tuned on the LAION-ART dataset. Advanced training techniques were used to speed up training, improve performance, and achieve better inference quality compared to similar models like Stable Diffusion v1-4 and Stable Diffusion 2.1. Model inputs and outputs DeciDiffusion-v1-0 is a diffusion-based text-to-image generation model. It takes text prompts as input and generates corresponding images as output. The model uses a Variational Autoencoder (VAE) and CLIP's pre-trained Text Encoder as core architectural components, along with a more efficient U-Net-NAS module developed by Deci. Inputs Text prompt**: A text description of the desired image Outputs Generated image**: The model outputs a corresponding image based on the input text prompt Capabilities DeciDiffusion-v1-0 is capable of generating high-quality photorealistic images from text prompts. It was trained using advanced techniques like V-prediction and enforcing zero terminal SNR during training to improve sample efficiency and inference quality. The model is able to generate a wide variety of image types, including landscapes, objects, and scenes. What can I use it for? The DeciDiffusion-v1-0 model is intended for research purposes. Potential use cases include generating artworks, exploring the capabilities and limitations of generative models, and developing educational or creative tools. However, the model should not be used to create harmful, discriminatory, or otherwise problematic content. Things to try One key feature of DeciDiffusion-v1-0 is its improved computational efficiency compared to similar models. Developers can experiment with using the model for faster text-to-image generation, or explore ways to leverage the more efficient U-Net-NAS architecture in their own projects. Additionally, the model's strong performance on the LAION-ART dataset suggests it may be well-suited for artistic and creative applications.
Updated 5/28/2024
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DeciLM-6b-instruct
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DeciLM-6b-instruct is a 6 billion parameter language model developed by Deci that is optimized for short-form instruction following. It is built by fine-tuning the DeciLM 6B model on a subset of the OpenOrca dataset. The model uses an optimized transformer decoder architecture that includes variable Grouped-Query Attention, which allows for efficient processing while maintaining performance. Model inputs and outputs Inputs Natural language instructions or queries Outputs Coherent and relevant text responses to the provided inputs Capabilities DeciLM-6b-instruct is capable of following a wide range of instructions and generating appropriate responses. It can assist with tasks like answering questions, providing step-by-step instructions, and generating creative content. The model has demonstrated strong performance on benchmarks like ARC Challenge, BoolQ, and PIQA. What can I use it for? DeciLM-6b-instruct can be used for various commercial and research applications that require short-form instruction following in English. This includes virtual assistants, content generation, and task automation. The model can also be fine-tuned on additional data to adapt it to specific use cases or languages. For example, the DeciLM-7B-instruct model is a larger version of the DeciLM-6b-instruct model that has been fine-tuned for instruction following. Things to try One interesting aspect of DeciLM-6b-instruct is its use of variable Grouped-Query Attention, which allows it to maintain high performance while being computationally efficient. You could experiment with this model's ability to generate concise and accurate responses to a variety of instructions, and compare its performance to other instruction-following language models like Falcon-7B-Instruct or MPT-7B-Instruct. This could provide insights into the tradeoffs between model size, architecture, and instruction-following capabilities.
Updated 5/27/2024
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DeciLM-7B-instruct
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DeciLM-7B-instruct is a 7 billion parameter language model developed by Deci that has been fine-tuned for short-form instruction following. It is built by LoRA fine-tuning on the SlimOrca dataset. The model leverages an optimized transformer decoder architecture with variable Grouped-Query Attention to achieve strong performance and efficiency. Compared to similar models like DeciLM-6B-instruct and DeciLM-7B, DeciLM-7B-instruct offers enhanced instruction-following capabilities while retaining the speed and accuracy of its base model. Model inputs and outputs DeciLM-7B-instruct is a text generation model that takes prompts as input and generates relevant text outputs. It can be used for a variety of natural language tasks, including question answering, summarization, and open-ended conversation. Inputs Prompts**: Free-form text that the model uses as a starting point to generate relevant output. Outputs Generated text**: The model's response to the input prompt, which can range from a single sentence to multiple paragraphs depending on the task. Capabilities DeciLM-7B-instruct is highly capable at understanding and following instructions provided in natural language. It can break down complex tasks into step-by-step instructions, provide detailed explanations, and generate relevant text outputs. The model's strong performance and efficiency make it a compelling choice for a wide range of applications, from customer service chatbots to task-oriented virtual assistants. What can I use it for? DeciLM-7B-instruct is well-suited for commercial and research use cases that require a language model with strong instruction-following capabilities. Some potential applications include: Customer service**: The model can be used to power chatbots that can provide detailed, step-by-step instructions to assist customers with product usage, troubleshooting, and other queries. Virtual assistants**: By leveraging the model's ability to understand and follow instructions, virtual assistants can be developed to help users with a variety of tasks, from scheduling appointments to providing cooking instructions. Content generation**: The model can be used to generate high-quality, relevant content for websites, blogs, and other digital platforms, with the ability to follow specific instructions or guidelines. Things to try One interesting aspect of DeciLM-7B-instruct is its ability to break down complex tasks into clear, step-by-step instructions. Try providing the model with prompts that involve multi-step processes, such as "How do I bake a cake?" or "Walk me through the process of changing a tire." Observe how the model responds, noting the level of detail and the clarity of the instructions provided. Another interesting experiment would be to explore the model's ability to follow instructions that involve creative or open-ended tasks, such as "Write a short story about a talking giraffe" or "Design a poster for a new music festival." This can help demonstrate the model's flexibility and its capacity for generating diverse and engaging content.
Updated 5/28/2024