---
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: 65.0
      name: accuracy
      verified: false
    - type: accuracy-norm
      value: 71.5
      name: accuracy-norm
      verified: false
    - type: accuracy-norm
      value: 41.0
      name: accuracy-norm
      verified: false
    - type: accuracy-norm
      value: 79.1
      name: accuracy-norm
      verified: false
    - type: accuracy-norm
      value: 65.0
      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](https://huggingface.co/ibm/PowerMoE-3b).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).


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## Prompt template


```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [PowerMoE-3b-Q2_K.gguf](https://huggingface.co/tensorblock/PowerMoE-3b-GGUF/blob/main/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](https://huggingface.co/tensorblock/PowerMoE-3b-GGUF/blob/main/PowerMoE-3b-Q3_K_S.gguf) | Q3_K_S | 1.386 GB | very small, high quality loss |
| [PowerMoE-3b-Q3_K_M.gguf](https://huggingface.co/tensorblock/PowerMoE-3b-GGUF/blob/main/PowerMoE-3b-Q3_K_M.gguf) | Q3_K_M | 1.531 GB | very small, high quality loss |
| [PowerMoE-3b-Q3_K_L.gguf](https://huggingface.co/tensorblock/PowerMoE-3b-GGUF/blob/main/PowerMoE-3b-Q3_K_L.gguf) | Q3_K_L | 1.652 GB | small, substantial quality loss |
| [PowerMoE-3b-Q4_0.gguf](https://huggingface.co/tensorblock/PowerMoE-3b-GGUF/blob/main/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](https://huggingface.co/tensorblock/PowerMoE-3b-GGUF/blob/main/PowerMoE-3b-Q4_K_S.gguf) | Q4_K_S | 1.809 GB | small, greater quality loss |
| [PowerMoE-3b-Q4_K_M.gguf](https://huggingface.co/tensorblock/PowerMoE-3b-GGUF/blob/main/PowerMoE-3b-Q4_K_M.gguf) | Q4_K_M | 1.918 GB | medium, balanced quality - recommended |
| [PowerMoE-3b-Q5_0.gguf](https://huggingface.co/tensorblock/PowerMoE-3b-GGUF/blob/main/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](https://huggingface.co/tensorblock/PowerMoE-3b-GGUF/blob/main/PowerMoE-3b-Q5_K_S.gguf) | Q5_K_S | 2.178 GB | large, low quality loss - recommended |
| [PowerMoE-3b-Q5_K_M.gguf](https://huggingface.co/tensorblock/PowerMoE-3b-GGUF/blob/main/PowerMoE-3b-Q5_K_M.gguf) | Q5_K_M | 2.242 GB | large, very low quality loss - recommended |
| [PowerMoE-3b-Q6_K.gguf](https://huggingface.co/tensorblock/PowerMoE-3b-GGUF/blob/main/PowerMoE-3b-Q6_K.gguf) | Q6_K | 2.586 GB | very large, extremely low quality loss |
| [PowerMoE-3b-Q8_0.gguf](https://huggingface.co/tensorblock/PowerMoE-3b-GGUF/blob/main/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

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
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:

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