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---
library_name: transformers
pipeline_tag: text-generation
base_model: aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct
language:
- en
- zh
- vi
- id
- th
- fil
- ta
- ms
- km
- lo
- my
- jv
- su
license: llama3.1
tags:
- TensorBlock
- GGUF
---
<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>
## aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct - GGUF
This repo contains GGUF format model files for [aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct](https://huggingface.co/aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4823](https://github.com/ggml-org/llama.cpp/commit/5bbe6a9fe9a8796a9389c85accec89dbc4d91e39).
## 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|>
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-cpt-sea-lionv3-instruct-Q2_K.gguf](https://huggingface.co/tensorblock/llama3.1-8b-cpt-sea-lionv3-instruct-GGUF/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q2_K.gguf) | Q2_K | 3.179 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama3.1-8b-cpt-sea-lionv3-instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/llama3.1-8b-cpt-sea-lionv3-instruct-GGUF/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q3_K_S.gguf) | Q3_K_S | 3.665 GB | very small, high quality loss |
| [llama3.1-8b-cpt-sea-lionv3-instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/llama3.1-8b-cpt-sea-lionv3-instruct-GGUF/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q3_K_M.gguf) | Q3_K_M | 4.019 GB | very small, high quality loss |
| [llama3.1-8b-cpt-sea-lionv3-instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/llama3.1-8b-cpt-sea-lionv3-instruct-GGUF/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q3_K_L.gguf) | Q3_K_L | 4.322 GB | small, substantial quality loss |
| [llama3.1-8b-cpt-sea-lionv3-instruct-Q4_0.gguf](https://huggingface.co/tensorblock/llama3.1-8b-cpt-sea-lionv3-instruct-GGUF/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q4_0.gguf) | Q4_0 | 4.661 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llama3.1-8b-cpt-sea-lionv3-instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/llama3.1-8b-cpt-sea-lionv3-instruct-GGUF/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q4_K_S.gguf) | Q4_K_S | 4.693 GB | small, greater quality loss |
| [llama3.1-8b-cpt-sea-lionv3-instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/llama3.1-8b-cpt-sea-lionv3-instruct-GGUF/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q4_K_M.gguf) | Q4_K_M | 4.921 GB | medium, balanced quality - recommended |
| [llama3.1-8b-cpt-sea-lionv3-instruct-Q5_0.gguf](https://huggingface.co/tensorblock/llama3.1-8b-cpt-sea-lionv3-instruct-GGUF/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q5_0.gguf) | Q5_0 | 5.599 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llama3.1-8b-cpt-sea-lionv3-instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/llama3.1-8b-cpt-sea-lionv3-instruct-GGUF/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q5_K_S.gguf) | Q5_K_S | 5.599 GB | large, low quality loss - recommended |
| [llama3.1-8b-cpt-sea-lionv3-instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/llama3.1-8b-cpt-sea-lionv3-instruct-GGUF/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q5_K_M.gguf) | Q5_K_M | 5.733 GB | large, very low quality loss - recommended |
| [llama3.1-8b-cpt-sea-lionv3-instruct-Q6_K.gguf](https://huggingface.co/tensorblock/llama3.1-8b-cpt-sea-lionv3-instruct-GGUF/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q6_K.gguf) | Q6_K | 6.596 GB | very large, extremely low quality loss |
| [llama3.1-8b-cpt-sea-lionv3-instruct-Q8_0.gguf](https://huggingface.co/tensorblock/llama3.1-8b-cpt-sea-lionv3-instruct-GGUF/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-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/llama3.1-8b-cpt-sea-lionv3-instruct-GGUF --include "llama3.1-8b-cpt-sea-lionv3-instruct-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-cpt-sea-lionv3-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|