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2024-11-18 23:26:36 +00:00
---
inference: false
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- llama
- TensorBlock
- GGUF
datasets:
- LDJnr/Capybara
- jondurbin/airoboros-3.2
- unalignment/toxic-dpo-v0.1
- LDJnr/Verified-Camel
- HuggingFaceH4/no_robots
- Doctor-Shotgun/no-robots-sharegpt
- Doctor-Shotgun/capybara-sharegpt
base_model: Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct
---
<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>
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## Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct - GGUF
This repo contains GGUF format model files for [Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct](https://huggingface.co/Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct).
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).
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## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
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<th colspan="2" style="font-size: 25px;">Forge</th>
</tr>
<tr>
<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>
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</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>
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<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>
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</table>
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## Prompt template
```
<s>
### Instruction:
{system_prompt}
### Input:
{prompt}
### Response:
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [TinyLlama-1.1B-32k-Instruct-Q2_K.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-32k-Instruct-GGUF/blob/main/TinyLlama-1.1B-32k-Instruct-Q2_K.gguf) | Q2_K | 0.402 GB | smallest, significant quality loss - not recommended for most purposes |
| [TinyLlama-1.1B-32k-Instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-32k-Instruct-GGUF/blob/main/TinyLlama-1.1B-32k-Instruct-Q3_K_S.gguf) | Q3_K_S | 0.465 GB | very small, high quality loss |
| [TinyLlama-1.1B-32k-Instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-32k-Instruct-GGUF/blob/main/TinyLlama-1.1B-32k-Instruct-Q3_K_M.gguf) | Q3_K_M | 0.511 GB | very small, high quality loss |
| [TinyLlama-1.1B-32k-Instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-32k-Instruct-GGUF/blob/main/TinyLlama-1.1B-32k-Instruct-Q3_K_L.gguf) | Q3_K_L | 0.551 GB | small, substantial quality loss |
| [TinyLlama-1.1B-32k-Instruct-Q4_0.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-32k-Instruct-GGUF/blob/main/TinyLlama-1.1B-32k-Instruct-Q4_0.gguf) | Q4_0 | 0.593 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [TinyLlama-1.1B-32k-Instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-32k-Instruct-GGUF/blob/main/TinyLlama-1.1B-32k-Instruct-Q4_K_S.gguf) | Q4_K_S | 0.596 GB | small, greater quality loss |
| [TinyLlama-1.1B-32k-Instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-32k-Instruct-GGUF/blob/main/TinyLlama-1.1B-32k-Instruct-Q4_K_M.gguf) | Q4_K_M | 0.622 GB | medium, balanced quality - recommended |
| [TinyLlama-1.1B-32k-Instruct-Q5_0.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-32k-Instruct-GGUF/blob/main/TinyLlama-1.1B-32k-Instruct-Q5_0.gguf) | Q5_0 | 0.713 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [TinyLlama-1.1B-32k-Instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-32k-Instruct-GGUF/blob/main/TinyLlama-1.1B-32k-Instruct-Q5_K_S.gguf) | Q5_K_S | 0.713 GB | large, low quality loss - recommended |
| [TinyLlama-1.1B-32k-Instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-32k-Instruct-GGUF/blob/main/TinyLlama-1.1B-32k-Instruct-Q5_K_M.gguf) | Q5_K_M | 0.728 GB | large, very low quality loss - recommended |
| [TinyLlama-1.1B-32k-Instruct-Q6_K.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-32k-Instruct-GGUF/blob/main/TinyLlama-1.1B-32k-Instruct-Q6_K.gguf) | Q6_K | 0.841 GB | very large, extremely low quality loss |
| [TinyLlama-1.1B-32k-Instruct-Q8_0.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-32k-Instruct-GGUF/blob/main/TinyLlama-1.1B-32k-Instruct-Q8_0.gguf) | Q8_0 | 1.089 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/TinyLlama-1.1B-32k-Instruct-GGUF --include "TinyLlama-1.1B-32k-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/TinyLlama-1.1B-32k-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
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