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upstage_llama-30b-instruct-2048-GGUF

Text Generation
GGUF
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upstage
llama
instruct
instruction
TensorBlock
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Model card Files
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upstage_llama-30b-instruct-2048-GGUF
File size: 7,134 Bytes
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---
datasets:
- sciq
- metaeval/ScienceQA_text_only
- GAIR/lima
- Open-Orca/OpenOrca
- openbookqa
language:
- en
tags:
- upstage
- llama
- instruct
- instruction
- TensorBlock
- GGUF
pipeline_tag: text-generation
base_model: upstage/llama-30b-instruct-2048
---

<div style="width: auto; margin-left: auto; margin-right: auto">
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## upstage/llama-30b-instruct-2048 - GGUF

This repo contains GGUF format model files for [upstage/llama-30b-instruct-2048](https://huggingface.co/upstage/llama-30b-instruct-2048).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5165](https://github.com/ggml-org/llama.cpp/commit/1d735c0b4fa0551c51c2f4ac888dd9a01f447985).

## Our projects
<table border="1" cellspacing="0" cellpadding="10">
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    <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;
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      ">🚀 Try it now! 🚀</a>
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  <tr>
    <th style="font-size: 25px;">Awesome MCP Servers</th>
    <th style="font-size: 25px;">TensorBlock Studio</th>
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    <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>
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    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
    <th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
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      <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
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        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;
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        border-radius: 6px;
        font-weight: bold;
        font-family: sans-serif;
      ">👀 See what we built 👀</a>
    </th>
  </tr>
</table>

## Prompt template

```
Unable to determine prompt format automatically. Please check the original model repository for the correct prompt format.
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [llama-30b-instruct-2048-Q2_K.gguf](https://huggingface.co/tensorblock/upstage_llama-30b-instruct-2048-GGUF/blob/main/llama-30b-instruct-2048-Q2_K.gguf) | Q2_K | 12.049 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama-30b-instruct-2048-Q3_K_S.gguf](https://huggingface.co/tensorblock/upstage_llama-30b-instruct-2048-GGUF/blob/main/llama-30b-instruct-2048-Q3_K_S.gguf) | Q3_K_S | 14.064 GB | very small, high quality loss |
| [llama-30b-instruct-2048-Q3_K_M.gguf](https://huggingface.co/tensorblock/upstage_llama-30b-instruct-2048-GGUF/blob/main/llama-30b-instruct-2048-Q3_K_M.gguf) | Q3_K_M | 15.776 GB | very small, high quality loss |
| [llama-30b-instruct-2048-Q3_K_L.gguf](https://huggingface.co/tensorblock/upstage_llama-30b-instruct-2048-GGUF/blob/main/llama-30b-instruct-2048-Q3_K_L.gguf) | Q3_K_L | 17.280 GB | small, substantial quality loss |
| [llama-30b-instruct-2048-Q4_0.gguf](https://huggingface.co/tensorblock/upstage_llama-30b-instruct-2048-GGUF/blob/main/llama-30b-instruct-2048-Q4_0.gguf) | Q4_0 | 18.356 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llama-30b-instruct-2048-Q4_K_S.gguf](https://huggingface.co/tensorblock/upstage_llama-30b-instruct-2048-GGUF/blob/main/llama-30b-instruct-2048-Q4_K_S.gguf) | Q4_K_S | 18.482 GB | small, greater quality loss |
| [llama-30b-instruct-2048-Q4_K_M.gguf](https://huggingface.co/tensorblock/upstage_llama-30b-instruct-2048-GGUF/blob/main/llama-30b-instruct-2048-Q4_K_M.gguf) | Q4_K_M | 19.621 GB | medium, balanced quality - recommended |
| [llama-30b-instruct-2048-Q5_0.gguf](https://huggingface.co/tensorblock/upstage_llama-30b-instruct-2048-GGUF/blob/main/llama-30b-instruct-2048-Q5_0.gguf) | Q5_0 | 22.395 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llama-30b-instruct-2048-Q5_K_S.gguf](https://huggingface.co/tensorblock/upstage_llama-30b-instruct-2048-GGUF/blob/main/llama-30b-instruct-2048-Q5_K_S.gguf) | Q5_K_S | 22.395 GB | large, low quality loss - recommended |
| [llama-30b-instruct-2048-Q5_K_M.gguf](https://huggingface.co/tensorblock/upstage_llama-30b-instruct-2048-GGUF/blob/main/llama-30b-instruct-2048-Q5_K_M.gguf) | Q5_K_M | 23.047 GB | large, very low quality loss - recommended |
| [llama-30b-instruct-2048-Q6_K.gguf](https://huggingface.co/tensorblock/upstage_llama-30b-instruct-2048-GGUF/blob/main/llama-30b-instruct-2048-Q6_K.gguf) | Q6_K | 26.687 GB | very large, extremely low quality loss |
| [llama-30b-instruct-2048-Q8_0.gguf](https://huggingface.co/tensorblock/upstage_llama-30b-instruct-2048-GGUF/blob/main/llama-30b-instruct-2048-Q8_0.gguf) | Q8_0 | 34.565 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/upstage_llama-30b-instruct-2048-GGUF --include "llama-30b-instruct-2048-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/upstage_llama-30b-instruct-2048-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```