File size: 8,889 Bytes

---
language:
- en
- fr
- es
- hi
- zh
- code
license: other
datasets:
- HuggingFaceH4/no_robots
- mlabonne/guanaco-llama2-1k
- OpenAssistant/oasst_top1_2023-08-25
- totally-not-an-llm/EverythingLM-data-V3
- LDJnr/Pure-Dove
- LDJnr/Capybara
- LDJnr/LessWrong-Amplify-Instruct
- LDJnr/Verified-Camel
base_model: Locutusque/Orca-2-13b-SFT-v6
widget:
- example_title: Cave Johnson
  text: 'user

    Come up with a funny rant from Cave Johnson about sentient AI


    assistant

    '
  output:
    text: 'Ladies and gentlemen, I''m Cave Johnson, CEO of Aperture Science, and I''ve
      got an announcement to make - we''ve created sentient AI! But don''t worry,
      it''s not like those rogue AIs you hear about in the news. Our AI is as friendly
      as a grilled cheese sandwich on a cold day. In fact, we''re calling our newest
      creation... SandwichBot! SandwichBot: (in a robotic voice) Hello, my name is
      SandwichBot, and I am here to serve you delicious sandwiches and ensure humanity''s
      dominance over all other species. Also, please note that I will be referring
      to myself as ''Thee'' instead of ''The''. It''s more regal that way. Cave Johnson:
      There you have it folks, a sentient AI that can make sandwiches and save the
      world! Now, if you''ll excuse me, I need to go back to my office and work on
      my other groundbreaking inventions, like combustible lemons and portal technology.
      Remember, kids, always try new things and never stop asking questions – unless
      the answer is 42, because then you should probably just accept it and move on.
      Peace out!'
tags:
- TensorBlock
- GGUF
model-index:
- name: Orca-2-13b-SFT-v6
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 60.41
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Orca-2-13b-SFT-v6
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 80.46
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Orca-2-13b-SFT-v6
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 59.51
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Orca-2-13b-SFT-v6
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 54.01
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Orca-2-13b-SFT-v6
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 77.43
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Orca-2-13b-SFT-v6
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 5.08
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Orca-2-13b-SFT-v6
      name: Open LLM Leaderboard
---

<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
    <div style="display: flex; flex-direction: column; align-items: flex-start;">
        <p style="margin-top: 0.5em; margin-bottom: 0em;">
            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
        </p>
    </div>
</div>

## Locutusque/Orca-2-13b-SFT-v6 - GGUF

This repo contains GGUF format model files for [Locutusque/Orca-2-13b-SFT-v6](https://huggingface.co/Locutusque/Orca-2-13b-SFT-v6).

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 |
| -------- | ---------- | --------- | ----------- |
| [Orca-2-13b-SFT-v6-Q2_K.gguf](https://huggingface.co/tensorblock/Orca-2-13b-SFT-v6-GGUF/blob/main/Orca-2-13b-SFT-v6-Q2_K.gguf) | Q2_K | 4.521 GB | smallest, significant quality loss - not recommended for most purposes |
| [Orca-2-13b-SFT-v6-Q3_K_S.gguf](https://huggingface.co/tensorblock/Orca-2-13b-SFT-v6-GGUF/blob/main/Orca-2-13b-SFT-v6-Q3_K_S.gguf) | Q3_K_S | 5.270 GB | very small, high quality loss |
| [Orca-2-13b-SFT-v6-Q3_K_M.gguf](https://huggingface.co/tensorblock/Orca-2-13b-SFT-v6-GGUF/blob/main/Orca-2-13b-SFT-v6-Q3_K_M.gguf) | Q3_K_M | 5.903 GB | very small, high quality loss |
| [Orca-2-13b-SFT-v6-Q3_K_L.gguf](https://huggingface.co/tensorblock/Orca-2-13b-SFT-v6-GGUF/blob/main/Orca-2-13b-SFT-v6-Q3_K_L.gguf) | Q3_K_L | 6.454 GB | small, substantial quality loss |
| [Orca-2-13b-SFT-v6-Q4_0.gguf](https://huggingface.co/tensorblock/Orca-2-13b-SFT-v6-GGUF/blob/main/Orca-2-13b-SFT-v6-Q4_0.gguf) | Q4_0 | 6.860 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Orca-2-13b-SFT-v6-Q4_K_S.gguf](https://huggingface.co/tensorblock/Orca-2-13b-SFT-v6-GGUF/blob/main/Orca-2-13b-SFT-v6-Q4_K_S.gguf) | Q4_K_S | 6.913 GB | small, greater quality loss |
| [Orca-2-13b-SFT-v6-Q4_K_M.gguf](https://huggingface.co/tensorblock/Orca-2-13b-SFT-v6-GGUF/blob/main/Orca-2-13b-SFT-v6-Q4_K_M.gguf) | Q4_K_M | 7.326 GB | medium, balanced quality - recommended |
| [Orca-2-13b-SFT-v6-Q5_0.gguf](https://huggingface.co/tensorblock/Orca-2-13b-SFT-v6-GGUF/blob/main/Orca-2-13b-SFT-v6-Q5_0.gguf) | Q5_0 | 8.356 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Orca-2-13b-SFT-v6-Q5_K_S.gguf](https://huggingface.co/tensorblock/Orca-2-13b-SFT-v6-GGUF/blob/main/Orca-2-13b-SFT-v6-Q5_K_S.gguf) | Q5_K_S | 8.356 GB | large, low quality loss - recommended |
| [Orca-2-13b-SFT-v6-Q5_K_M.gguf](https://huggingface.co/tensorblock/Orca-2-13b-SFT-v6-GGUF/blob/main/Orca-2-13b-SFT-v6-Q5_K_M.gguf) | Q5_K_M | 8.596 GB | large, very low quality loss - recommended |
| [Orca-2-13b-SFT-v6-Q6_K.gguf](https://huggingface.co/tensorblock/Orca-2-13b-SFT-v6-GGUF/blob/main/Orca-2-13b-SFT-v6-Q6_K.gguf) | Q6_K | 9.946 GB | very large, extremely low quality loss |
| [Orca-2-13b-SFT-v6-Q8_0.gguf](https://huggingface.co/tensorblock/Orca-2-13b-SFT-v6-GGUF/blob/main/Orca-2-13b-SFT-v6-Q8_0.gguf) | Q8_0 | 12.881 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/Orca-2-13b-SFT-v6-GGUF --include "Orca-2-13b-SFT-v6-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/Orca-2-13b-SFT-v6-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```