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PowerMoE-3b-GGUF

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metadata
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
  - TensorBlock
  - GGUF
base_model: ibm/PowerMoE-3b
model-index:
  - name: ibm/PowerMoE-3b
    results:
      - task:
          type: text-generation
        dataset:
          name: ARC
          type: lm-eval-harness
        metrics:
          - type: accuracy-norm
            value: 58.1
            name: accuracy-norm
            verified: false
          - type: accuracy
            value: 65
            name: accuracy
            verified: false
          - type: accuracy-norm
            value: 71.5
            name: accuracy-norm
            verified: false
          - type: accuracy-norm
            value: 41
            name: accuracy-norm
            verified: false
          - type: accuracy-norm
            value: 79.1
            name: accuracy-norm
            verified: false
          - type: accuracy-norm
            value: 65
            name: accuracy-norm
            verified: false
          - type: accuracy
            value: 42.8
            name: accuracy
            verified: false
          - type: accuracy
            value: 25.9
            name: accuracy
            verified: false
          - type: accuracy
            value: 14.8
            name: accuracy
            verified: false
      - task:
          type: text-generation
        dataset:
          name: humaneval
          type: bigcode-eval
        metrics:
          - type: pass@1
            value: 20.1
            name: pass@1
            verified: false
          - type: pass@1
            value: 32.4
            name: pass@1
            verified: false
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ibm/PowerMoE-3b - GGUF

This repo contains GGUF format model files for ibm/PowerMoE-3b.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Prompt template

Model file specification

Filename Quant type File Size Description
PowerMoE-3b-Q2_K.gguf Q2_K 1.179 GB smallest, significant quality loss - not recommended for most purposes
PowerMoE-3b-Q3_K_S.gguf Q3_K_S 1.386 GB very small, high quality loss
PowerMoE-3b-Q3_K_M.gguf Q3_K_M 1.531 GB very small, high quality loss
PowerMoE-3b-Q3_K_L.gguf Q3_K_L 1.652 GB small, substantial quality loss
PowerMoE-3b-Q4_0.gguf Q4_0 1.794 GB legacy; small, very high quality loss - prefer using Q3_K_M
PowerMoE-3b-Q4_K_S.gguf Q4_K_S 1.809 GB small, greater quality loss
PowerMoE-3b-Q4_K_M.gguf Q4_K_M 1.918 GB medium, balanced quality - recommended
PowerMoE-3b-Q5_0.gguf Q5_0 2.178 GB legacy; medium, balanced quality - prefer using Q4_K_M
PowerMoE-3b-Q5_K_S.gguf Q5_K_S 2.178 GB large, low quality loss - recommended
PowerMoE-3b-Q5_K_M.gguf Q5_K_M 2.242 GB large, very low quality loss - recommended
PowerMoE-3b-Q6_K.gguf Q6_K 2.586 GB very large, extremely low quality loss
PowerMoE-3b-Q8_0.gguf Q8_0 3.346 GB very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/PowerMoE-3b-GGUF --include "PowerMoE-3b-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/PowerMoE-3b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'