WizardLM-13B-V1.2

Maintainer: WizardLMTeam

Total Score

217

Last updated 6/11/2024

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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

The WizardLM-13B-V1.2 model is a large pre-trained language model developed by the WizardLM team. It is a full-weight version of the WizardLM-13B model, which is based on the Llama-2 13b model. The WizardLM team has also released larger versions of the model, including the WizardLM-70B-V1.0 which slightly outperforms some closed-source LLMs on benchmarks.

Model inputs and outputs

The WizardLM-13B-V1.2 model is a text-to-text transformer that can be used for a variety of natural language processing tasks. It takes text prompts as input and generates relevant text responses.

Inputs

  • Text prompts or instructions for the model to follow

Outputs

  • Coherent, multi-sentence text responses that address the input prompts

Capabilities

The WizardLM-13B-V1.2 model is capable of following complex instructions and engaging in open-ended conversations. It has been shown to outperform other large language models on benchmarks like MT-Bench, AlpacaEval, and WizardEval. For example, the model achieves 36.6 pass@1 on the HumanEval benchmark, demonstrating its ability to generate solutions to complex coding problems.

What can I use it for?

The WizardLM-13B-V1.2 model could be useful for a wide range of applications that require natural language understanding and generation, such as:

  • Engaging in open-ended conversations and answering questions
  • Providing detailed and helpful responses to instructions and prompts
  • Assisting with coding and software development tasks
  • Generating human-like text for creative writing or content creation

Things to try

One interesting thing to try with the WizardLM-13B-V1.2 model is to provide it with complex, multi-step instructions and observe how it responds. The model's ability to follow intricate prompts and generate coherent, detailed responses is a key capability. You could also try using the model for tasks like code generation or mathematical reasoning, as the WizardLM team has shown the model's strong performance on benchmarks like HumanEval and GSM8k.



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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WizardLM-70B-V1.0

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