DeciLM-6b

Maintainer: Deci

Total Score

234

Last updated 5/27/2024

💬

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

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.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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