--- license: apache-2.0 tags: - MoE - merge - mergekit - Mistral - Microsoft/WizardLM-2-7B --- # WizardLM-2-4x7B-MoE WizardLM-2-4x7B-MoE is an experimental MoE model made with [Mergekit](https://github.com/arcee-ai/mergekit). It was made by combining four [WizardLM-2-7B](https://huggingface.co./microsoft/WizardLM-2-7B) models using the random gate mode. Please be sure to set experts per token to 4 for the best results! Context length should be the same as Mistral-7B-Instruct-v0.1 (8k tokens). For instruction templates, Vicuna-v1.1 is recommended. # Quanitized versions EXL2 (for fast GPU-only inference):
8_0bpw: https://huggingface.co./Skylaude/WizardLM-2-4x7B-MoE-exl2-8_0bpw (~ 25 GB vram)
6_0bpw: https://huggingface.co./Skylaude/WizardLM-2-4x7B-MoE-exl2-6_0bpw (~ 19 GB vram)
5_0bpw: https://huggingface.co./Skylaude/WizardLM-2-4x7B-MoE-exl2-5_0bpw (~ 16 GB vram)
4_25bpw: https://huggingface.co./Skylaude/WizardLM-2-4x7B-MoE-exl2-4_25bpw (~ 14 GB vram)
3_5bpw: https://huggingface.co./Skylaude/WizardLM-2-4x7B-MoE-exl2-3_5bpw (~ 12 GB vram)
3_0bpw: https://huggingface.co./Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw (~ 11 GB vram) GGUF (for mixed GPU+CPU inference or CPU-only inference):
https://huggingface.co./mradermacher/WizardLM-2-4x7B-MoE-GGUF
Thanks to [Michael Radermacher](https://huggingface.co./mradermacher) for making these quants! # Evaluation I don't expect this model to be that great since it's something that I made as an experiment. However, I will submit it to the Open LLM Leaderboard to see how it matches up against some other models (particularly WizardLM-2-7B and WizardLM-2-70B). # Mergekit config ``` base_model: models/WizardLM-2-7B gate_mode: random dtype: float16 experts_per_token: 4 experts: - source_model: models/WizardLM-2-7B - source_model: models/WizardLM-2-7B - source_model: models/WizardLM-2-7B - source_model: models/WizardLM-2-7B ```