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Llama3.1-8B-ShiningValiant2-GGUF

Text Generation
GGUF
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Llama3.1-8B-ShiningValiant2-GGUF
File size: 11,222 Bytes
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---
language:
- en
pipeline_tag: text-generation
tags:
- shining-valiant
- shining-valiant-2
- valiant
- valiant-labs
- llama
- llama-3.1
- llama-3.1-instruct
- llama-3.1-instruct-8b
- llama-3
- llama-3-instruct
- llama-3-instruct-8b
- 8b
- science
- physics
- biology
- chemistry
- compsci
- computer-science
- engineering
- technical
- conversational
- chat
- instruct
- TensorBlock
- GGUF
base_model: ValiantLabs/Llama3.1-8B-ShiningValiant2
datasets:
- sequelbox/Celestia
- sequelbox/Spurline
- sequelbox/Supernova
model_type: llama
license: llama3.1
model-index:
- name: Llama3.1-8B-ShiningValiant2
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-Shot)
      type: Winogrande
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 75.85
      name: acc
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU College Biology (5-Shot)
      type: MMLU
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 68.75
      name: acc
    - type: acc
      value: 73.23
      name: acc
    - type: acc
      value: 46.0
      name: acc
    - type: acc
      value: 44.33
      name: acc
    - type: acc
      value: 53.19
      name: acc
    - type: acc
      value: 37.25
      name: acc
    - type: acc
      value: 42.38
      name: acc
    - type: acc
      value: 56.0
      name: acc
    - type: acc
      value: 63.0
      name: acc
    - type: acc
      value: 63.16
      name: acc
  - 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: 65.24
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
      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: 26.35
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
      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: 11.63
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
      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: 8.95
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
      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: 7.19
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
      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: 26.38
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
      name: Open LLM Leaderboard
---

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## ValiantLabs/Llama3.1-8B-ShiningValiant2 - GGUF

This repo contains GGUF format model files for [ValiantLabs/Llama3.1-8B-ShiningValiant2](https://huggingface.co/ValiantLabs/Llama3.1-8B-ShiningValiant2).

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).


## Our projects
<table border="1" cellspacing="0" cellpadding="10">
  <tr>
    <th colspan="2" style="font-size: 25px;">Forge</th>
  </tr>
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    <th colspan="2">
      <img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
    </th>
  </tr>
  <tr>
    <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
  </tr>
  <tr>
    <th colspan="2">
      <a href="https://github.com/TensorBlock/forge" target="_blank" style="
        display: inline-block;
        padding: 8px 16px;
        background-color: #FF7F50;
        color: white;
        text-decoration: none;
        border-radius: 6px;
        font-weight: bold;
        font-family: sans-serif;
      ">🚀 Try it now! 🚀</a>
    </th>
  </tr>

  <tr>
    <th style="font-size: 25px;">Awesome MCP Servers</th>
    <th style="font-size: 25px;">TensorBlock Studio</th>
  </tr>
  <tr>
    <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
    <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
  </tr>
  <tr>
    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
    <th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
  </tr>
  <tr>
    <th>
      <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
        display: inline-block;
        padding: 8px 16px;
        background-color: #FF7F50;
        color: white;
        text-decoration: none;
        border-radius: 6px;
        font-weight: bold;
        font-family: sans-serif;
      ">👀 See what we built 👀</a>
    </th>
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      <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
        display: inline-block;
        padding: 8px 16px;
        background-color: #FF7F50;
        color: white;
        text-decoration: none;
        border-radius: 6px;
        font-weight: bold;
        font-family: sans-serif;
      ">👀 See what we built 👀</a>
    </th>
  </tr>
</table>
## Prompt template


```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Cutting Knowledge Date: December 2023
Today Date: 26 Jul 2024

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Llama3.1-8B-ShiningValiant2-Q2_K.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/blob/main/Llama3.1-8B-ShiningValiant2-Q2_K.gguf) | Q2_K | 2.961 GB | smallest, significant quality loss - not recommended for most purposes |
| [Llama3.1-8B-ShiningValiant2-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/blob/main/Llama3.1-8B-ShiningValiant2-Q3_K_S.gguf) | Q3_K_S | 3.413 GB | very small, high quality loss |
| [Llama3.1-8B-ShiningValiant2-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/blob/main/Llama3.1-8B-ShiningValiant2-Q3_K_M.gguf) | Q3_K_M | 3.743 GB | very small, high quality loss |
| [Llama3.1-8B-ShiningValiant2-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/blob/main/Llama3.1-8B-ShiningValiant2-Q3_K_L.gguf) | Q3_K_L | 4.025 GB | small, substantial quality loss |
| [Llama3.1-8B-ShiningValiant2-Q4_0.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/blob/main/Llama3.1-8B-ShiningValiant2-Q4_0.gguf) | Q4_0 | 4.341 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Llama3.1-8B-ShiningValiant2-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/blob/main/Llama3.1-8B-ShiningValiant2-Q4_K_S.gguf) | Q4_K_S | 4.370 GB | small, greater quality loss |
| [Llama3.1-8B-ShiningValiant2-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/blob/main/Llama3.1-8B-ShiningValiant2-Q4_K_M.gguf) | Q4_K_M | 4.583 GB | medium, balanced quality - recommended |
| [Llama3.1-8B-ShiningValiant2-Q5_0.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/blob/main/Llama3.1-8B-ShiningValiant2-Q5_0.gguf) | Q5_0 | 5.215 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Llama3.1-8B-ShiningValiant2-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/blob/main/Llama3.1-8B-ShiningValiant2-Q5_K_S.gguf) | Q5_K_S | 5.215 GB | large, low quality loss - recommended |
| [Llama3.1-8B-ShiningValiant2-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/blob/main/Llama3.1-8B-ShiningValiant2-Q5_K_M.gguf) | Q5_K_M | 5.339 GB | large, very low quality loss - recommended |
| [Llama3.1-8B-ShiningValiant2-Q6_K.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/blob/main/Llama3.1-8B-ShiningValiant2-Q6_K.gguf) | Q6_K | 6.143 GB | very large, extremely low quality loss |
| [Llama3.1-8B-ShiningValiant2-Q8_0.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/blob/main/Llama3.1-8B-ShiningValiant2-Q8_0.gguf) | Q8_0 | 7.954 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/Llama3.1-8B-ShiningValiant2-GGUF --include "Llama3.1-8B-ShiningValiant2-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/Llama3.1-8B-ShiningValiant2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```