llama-3-8b-gpt-4o-ru1.0-GGUF/README.md

---
license: llama3
base_model: ruslandev/llama-3-8b-gpt-4o-ru1.0
tags:
- generated_from_trainer
- TensorBlock
- GGUF
datasets:
- ruslandev/tagengo-rus-gpt-4o
model-index:
- name: home/ubuntu/llm_training/axolotl/llama3-8b-gpt-4o-ru/output_llama3_8b_gpt_4o_ru
  results: []
---

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## ruslandev/llama-3-8b-gpt-4o-ru1.0 - GGUF

This repo contains GGUF format model files for [ruslandev/llama-3-8b-gpt-4o-ru1.0](https://huggingface.co/ruslandev/llama-3-8b-gpt-4o-ru1.0).

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 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="Project A" width="450"/></th>
    <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Project B" 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>
  <th>
    <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|>

{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 |
| -------- | ---------- | --------- | ----------- |
| [llama-3-8b-gpt-4o-ru1.0-Q2_K.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q2_K.gguf) | Q2_K | 3.179 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama-3-8b-gpt-4o-ru1.0-Q3_K_S.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q3_K_S.gguf) | Q3_K_S | 3.664 GB | very small, high quality loss |
| [llama-3-8b-gpt-4o-ru1.0-Q3_K_M.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q3_K_M.gguf) | Q3_K_M | 4.019 GB | very small, high quality loss |
| [llama-3-8b-gpt-4o-ru1.0-Q3_K_L.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q3_K_L.gguf) | Q3_K_L | 4.322 GB | small, substantial quality loss |
| [llama-3-8b-gpt-4o-ru1.0-Q4_0.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q4_0.gguf) | Q4_0 | 4.661 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llama-3-8b-gpt-4o-ru1.0-Q4_K_S.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q4_K_S.gguf) | Q4_K_S | 4.693 GB | small, greater quality loss |
| [llama-3-8b-gpt-4o-ru1.0-Q4_K_M.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q4_K_M.gguf) | Q4_K_M | 4.921 GB | medium, balanced quality - recommended |
| [llama-3-8b-gpt-4o-ru1.0-Q5_0.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q5_0.gguf) | Q5_0 | 5.599 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llama-3-8b-gpt-4o-ru1.0-Q5_K_S.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q5_K_S.gguf) | Q5_K_S | 5.599 GB | large, low quality loss - recommended |
| [llama-3-8b-gpt-4o-ru1.0-Q5_K_M.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q5_K_M.gguf) | Q5_K_M | 5.733 GB | large, very low quality loss - recommended |
| [llama-3-8b-gpt-4o-ru1.0-Q6_K.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q6_K.gguf) | Q6_K | 6.596 GB | very large, extremely low quality loss |
| [llama-3-8b-gpt-4o-ru1.0-Q8_0.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q8_0.gguf) | Q8_0 | 8.541 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/llama-3-8b-gpt-4o-ru1.0-GGUF --include "llama-3-8b-gpt-4o-ru1.0-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/llama-3-8b-gpt-4o-ru1.0-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```