---
license: apache-2.0
language:
- en
- zh
base_model: prithivMLmods/Gauss-Opus-14B-R999
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- trl
- vlm
- sft
- code
- math
- TensorBlock
- GGUF
model-index:
- name: Gauss-Opus-14B-R999
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: wis-k/instruction-following-eval
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 39.07
      name: averaged accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FGauss-Opus-14B-R999
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: SaylorTwift/bbh
      split: test
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 44.94
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FGauss-Opus-14B-R999
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: lighteval/MATH-Hard
      split: test
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 57.55
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FGauss-Opus-14B-R999
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 18.9
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FGauss-Opus-14B-R999
      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: 27.83
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FGauss-Opus-14B-R999
      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: 44.53
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FGauss-Opus-14B-R999
      name: Open LLM Leaderboard
---

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## prithivMLmods/Gauss-Opus-14B-R999 - GGUF

This repo contains GGUF format model files for [prithivMLmods/Gauss-Opus-14B-R999](https://huggingface.co/prithivMLmods/Gauss-Opus-14B-R999).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4882](https://github.com/ggml-org/llama.cpp/commit/be7c3034108473beda214fd1d7c98fd6a7a3bdf5).

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## Prompt template

```
<｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜><think>
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Gauss-Opus-14B-R999-Q2_K.gguf](https://huggingface.co/tensorblock/Gauss-Opus-14B-R999-GGUF/blob/main/Gauss-Opus-14B-R999-Q2_K.gguf) | Q2_K | 0.006 GB | smallest, significant quality loss - not recommended for most purposes |
| [Gauss-Opus-14B-R999-Q3_K_S.gguf](https://huggingface.co/tensorblock/Gauss-Opus-14B-R999-GGUF/blob/main/Gauss-Opus-14B-R999-Q3_K_S.gguf) | Q3_K_S | 0.006 GB | very small, high quality loss |
| [Gauss-Opus-14B-R999-Q3_K_M.gguf](https://huggingface.co/tensorblock/Gauss-Opus-14B-R999-GGUF/blob/main/Gauss-Opus-14B-R999-Q3_K_M.gguf) | Q3_K_M | 0.006 GB | very small, high quality loss |
| [Gauss-Opus-14B-R999-Q3_K_L.gguf](https://huggingface.co/tensorblock/Gauss-Opus-14B-R999-GGUF/blob/main/Gauss-Opus-14B-R999-Q3_K_L.gguf) | Q3_K_L | 0.006 GB | small, substantial quality loss |
| [Gauss-Opus-14B-R999-Q4_0.gguf](https://huggingface.co/tensorblock/Gauss-Opus-14B-R999-GGUF/blob/main/Gauss-Opus-14B-R999-Q4_0.gguf) | Q4_0 | 0.006 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Gauss-Opus-14B-R999-Q4_K_S.gguf](https://huggingface.co/tensorblock/Gauss-Opus-14B-R999-GGUF/blob/main/Gauss-Opus-14B-R999-Q4_K_S.gguf) | Q4_K_S | 0.006 GB | small, greater quality loss |
| [Gauss-Opus-14B-R999-Q4_K_M.gguf](https://huggingface.co/tensorblock/Gauss-Opus-14B-R999-GGUF/blob/main/Gauss-Opus-14B-R999-Q4_K_M.gguf) | Q4_K_M | 0.006 GB | medium, balanced quality - recommended |
| [Gauss-Opus-14B-R999-Q5_0.gguf](https://huggingface.co/tensorblock/Gauss-Opus-14B-R999-GGUF/blob/main/Gauss-Opus-14B-R999-Q5_0.gguf) | Q5_0 | 0.006 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Gauss-Opus-14B-R999-Q5_K_S.gguf](https://huggingface.co/tensorblock/Gauss-Opus-14B-R999-GGUF/blob/main/Gauss-Opus-14B-R999-Q5_K_S.gguf) | Q5_K_S | 0.006 GB | large, low quality loss - recommended |
| [Gauss-Opus-14B-R999-Q5_K_M.gguf](https://huggingface.co/tensorblock/Gauss-Opus-14B-R999-GGUF/blob/main/Gauss-Opus-14B-R999-Q5_K_M.gguf) | Q5_K_M | 0.006 GB | large, very low quality loss - recommended |
| [Gauss-Opus-14B-R999-Q6_K.gguf](https://huggingface.co/tensorblock/Gauss-Opus-14B-R999-GGUF/blob/main/Gauss-Opus-14B-R999-Q6_K.gguf) | Q6_K | 0.006 GB | very large, extremely low quality loss |
| [Gauss-Opus-14B-R999-Q8_0.gguf](https://huggingface.co/tensorblock/Gauss-Opus-14B-R999-GGUF/blob/main/Gauss-Opus-14B-R999-Q8_0.gguf) | Q8_0 | 0.006 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/Gauss-Opus-14B-R999-GGUF --include "Gauss-Opus-14B-R999-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/Gauss-Opus-14B-R999-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```