WizardLM: An Efficient and Effective Model for Complex Question-Answering

WizardLM is a large-scale language model based on the GPT-3 architecture, trained on diverse sources of text, such as books, web pages, and scientific articles. It is designed for complex question-answering tasks and has been shown to outperform existing models on several benchmarks.

The model is available in various sizes, ranging from the smallest version, with 125M parameters, to the largest version, with 13B parameters. Additionally, the model is available in quantised versions, which offer improved VRAM efficiency without sacrificing accuracy. The latest version of WizardLM uses llama.cpp's new 5-bit quantisation methods q5_0 and q5_1, making it the most VRAM efficient model with the best results so far. However, users should be aware that these new methods only work with the latest llama.cpp code and are not currently compatible with third-party UIs/utilities.

Users can access WizardLM on Hugging Face, where it is available in various versions and sizes, including the latest version with the new quantisation methods. Additionally, a demo is available on the WizardLM GitHub repository, which allows users to test the model's capabilities on various prompts.

The model has impressed many with its ability to accurately answer complex questions, all while never running out of VRAM even on high-end GPUs like the RTX 3080Ti. One user reported running the Oobabooga version, wizardLM-7B-GPTQ-4bit-128g.ooba.no-act-order.pt, at pre_layer 25, but noted that it was still VRAM-efficient enough to run without this parameter. However, some users have reported slow performance and poor outputs, despite running the 4-bit version. This may be due to compatibility issues with older GPUs, as one user inquired if their Nvidia 1080, with 8GB of VRAM, would be compatible.

In conclusion, WizardLM is an impressive model for complex question-answering that offers efficient and effective performance on high-end GPUs. Users should be aware of compatibility issues and the need for the latest llama.cpp code to run the latest quantisation methods.

Entities: WizardLM, GPT-3, Hugging Face, llama.cpp, VRAM, GPUs, RTX 3080Ti, Nvidia 1080.
Categories: Language model, question-answering, quantisation, VRAM efficiency, GPU performance, compatibility issues, Hugging Face, GitHub.

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