---
language:
- ko
- en
- zh
- ja
license: other
library_name: transformers
tags:
- pytorch
- TensorBlock
- GGUF
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
pipeline_tag: text-generation
base_model: beomi/gemma-mling-7b
model-index:
- name: gemma-mling-7b
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 20.29
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=beomi/gemma-mling-7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 17.63
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=beomi/gemma-mling-7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 4.15
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=beomi/gemma-mling-7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 0.0
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=beomi/gemma-mling-7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 6.85
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=beomi/gemma-mling-7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 18.14
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=beomi/gemma-mling-7b
      name: Open LLM Leaderboard
---

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## beomi/gemma-mling-7b - GGUF

This repo contains GGUF format model files for [beomi/gemma-mling-7b](https://huggingface.co/beomi/gemma-mling-7b).

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).

<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template

```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [gemma-mling-7b-Q2_K.gguf](https://huggingface.co/tensorblock/gemma-mling-7b-GGUF/blob/main/gemma-mling-7b-Q2_K.gguf) | Q2_K | 3.242 GB | smallest, significant quality loss - not recommended for most purposes |
| [gemma-mling-7b-Q3_K_S.gguf](https://huggingface.co/tensorblock/gemma-mling-7b-GGUF/blob/main/gemma-mling-7b-Q3_K_S.gguf) | Q3_K_S | 3.709 GB | very small, high quality loss |
| [gemma-mling-7b-Q3_K_M.gguf](https://huggingface.co/tensorblock/gemma-mling-7b-GGUF/blob/main/gemma-mling-7b-Q3_K_M.gguf) | Q3_K_M | 4.069 GB | very small, high quality loss |
| [gemma-mling-7b-Q3_K_L.gguf](https://huggingface.co/tensorblock/gemma-mling-7b-GGUF/blob/main/gemma-mling-7b-Q3_K_L.gguf) | Q3_K_L | 4.386 GB | small, substantial quality loss |
| [gemma-mling-7b-Q4_0.gguf](https://huggingface.co/tensorblock/gemma-mling-7b-GGUF/blob/main/gemma-mling-7b-Q4_0.gguf) | Q4_0 | 4.668 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [gemma-mling-7b-Q4_K_S.gguf](https://huggingface.co/tensorblock/gemma-mling-7b-GGUF/blob/main/gemma-mling-7b-Q4_K_S.gguf) | Q4_K_S | 4.700 GB | small, greater quality loss |
| [gemma-mling-7b-Q4_K_M.gguf](https://huggingface.co/tensorblock/gemma-mling-7b-GGUF/blob/main/gemma-mling-7b-Q4_K_M.gguf) | Q4_K_M | 4.964 GB | medium, balanced quality - recommended |
| [gemma-mling-7b-Q5_0.gguf](https://huggingface.co/tensorblock/gemma-mling-7b-GGUF/blob/main/gemma-mling-7b-Q5_0.gguf) | Q5_0 | 5.570 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [gemma-mling-7b-Q5_K_S.gguf](https://huggingface.co/tensorblock/gemma-mling-7b-GGUF/blob/main/gemma-mling-7b-Q5_K_S.gguf) | Q5_K_S | 5.570 GB | large, low quality loss - recommended |
| [gemma-mling-7b-Q5_K_M.gguf](https://huggingface.co/tensorblock/gemma-mling-7b-GGUF/blob/main/gemma-mling-7b-Q5_K_M.gguf) | Q5_K_M | 5.723 GB | large, very low quality loss - recommended |
| [gemma-mling-7b-Q6_K.gguf](https://huggingface.co/tensorblock/gemma-mling-7b-GGUF/blob/main/gemma-mling-7b-Q6_K.gguf) | Q6_K | 6.529 GB | very large, extremely low quality loss |
| [gemma-mling-7b-Q8_0.gguf](https://huggingface.co/tensorblock/gemma-mling-7b-GGUF/blob/main/gemma-mling-7b-Q8_0.gguf) | Q8_0 | 8.454 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/gemma-mling-7b-GGUF --include "gemma-mling-7b-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/gemma-mling-7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```