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---
library_name: transformers
license: llama3.1
base_model: barc0/Llama-3.1-ARC-Heavy-Transduction-8B
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- TensorBlock
- GGUF
datasets:
- barc0/transduction_heavy_100k_jsonl
- barc0/transduction_heavy_suggestfunction_100k_jsonl
model-index:
- name: heavy-barc-llama3.1-8b-ins-fft-transduction_lr1e-5_epoch3
results: []
---
<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%;">
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<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>
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## barc0/Llama-3.1-ARC-Heavy-Transduction-8B - GGUF
This repo contains GGUF format model files for [barc0/Llama-3.1-ARC-Heavy-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Heavy-Transduction-8B).
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>
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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>
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## 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 |
| -------- | ---------- | --------- | ----------- |
| [Llama-3.1-ARC-Heavy-Transduction-8B-Q2_K.gguf](https://huggingface.co/tensorblock/Llama-3.1-ARC-Heavy-Transduction-8B-GGUF/blob/main/Llama-3.1-ARC-Heavy-Transduction-8B-Q2_K.gguf) | Q2_K | 3.179 GB | smallest, significant quality loss - not recommended for most purposes |
| [Llama-3.1-ARC-Heavy-Transduction-8B-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama-3.1-ARC-Heavy-Transduction-8B-GGUF/blob/main/Llama-3.1-ARC-Heavy-Transduction-8B-Q3_K_S.gguf) | Q3_K_S | 3.665 GB | very small, high quality loss |
| [Llama-3.1-ARC-Heavy-Transduction-8B-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama-3.1-ARC-Heavy-Transduction-8B-GGUF/blob/main/Llama-3.1-ARC-Heavy-Transduction-8B-Q3_K_M.gguf) | Q3_K_M | 4.019 GB | very small, high quality loss |
| [Llama-3.1-ARC-Heavy-Transduction-8B-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama-3.1-ARC-Heavy-Transduction-8B-GGUF/blob/main/Llama-3.1-ARC-Heavy-Transduction-8B-Q3_K_L.gguf) | Q3_K_L | 4.322 GB | small, substantial quality loss |
| [Llama-3.1-ARC-Heavy-Transduction-8B-Q4_0.gguf](https://huggingface.co/tensorblock/Llama-3.1-ARC-Heavy-Transduction-8B-GGUF/blob/main/Llama-3.1-ARC-Heavy-Transduction-8B-Q4_0.gguf) | Q4_0 | 4.661 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Llama-3.1-ARC-Heavy-Transduction-8B-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama-3.1-ARC-Heavy-Transduction-8B-GGUF/blob/main/Llama-3.1-ARC-Heavy-Transduction-8B-Q4_K_S.gguf) | Q4_K_S | 4.693 GB | small, greater quality loss |
| [Llama-3.1-ARC-Heavy-Transduction-8B-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama-3.1-ARC-Heavy-Transduction-8B-GGUF/blob/main/Llama-3.1-ARC-Heavy-Transduction-8B-Q4_K_M.gguf) | Q4_K_M | 4.921 GB | medium, balanced quality - recommended |
| [Llama-3.1-ARC-Heavy-Transduction-8B-Q5_0.gguf](https://huggingface.co/tensorblock/Llama-3.1-ARC-Heavy-Transduction-8B-GGUF/blob/main/Llama-3.1-ARC-Heavy-Transduction-8B-Q5_0.gguf) | Q5_0 | 5.599 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Llama-3.1-ARC-Heavy-Transduction-8B-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama-3.1-ARC-Heavy-Transduction-8B-GGUF/blob/main/Llama-3.1-ARC-Heavy-Transduction-8B-Q5_K_S.gguf) | Q5_K_S | 5.599 GB | large, low quality loss - recommended |
| [Llama-3.1-ARC-Heavy-Transduction-8B-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama-3.1-ARC-Heavy-Transduction-8B-GGUF/blob/main/Llama-3.1-ARC-Heavy-Transduction-8B-Q5_K_M.gguf) | Q5_K_M | 5.733 GB | large, very low quality loss - recommended |
| [Llama-3.1-ARC-Heavy-Transduction-8B-Q6_K.gguf](https://huggingface.co/tensorblock/Llama-3.1-ARC-Heavy-Transduction-8B-GGUF/blob/main/Llama-3.1-ARC-Heavy-Transduction-8B-Q6_K.gguf) | Q6_K | 6.596 GB | very large, extremely low quality loss |
| [Llama-3.1-ARC-Heavy-Transduction-8B-Q8_0.gguf](https://huggingface.co/tensorblock/Llama-3.1-ARC-Heavy-Transduction-8B-GGUF/blob/main/Llama-3.1-ARC-Heavy-Transduction-8B-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-3.1-ARC-Heavy-Transduction-8B-GGUF --include "Llama-3.1-ARC-Heavy-Transduction-8B-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-3.1-ARC-Heavy-Transduction-8B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
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