---
license_link: https://huggingface.co/microsoft/Phi-3.5-mini-instruct/resolve/main/LICENSE
language:
- multilingual
library_name: transformers
license: mit
tags:
- unsloth
- transformers
- phi3
- phi
- TensorBlock
- GGUF
base_model: unsloth/Phi-3.5-mini-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>
<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>

## unsloth/Phi-3.5-mini-instruct - GGUF

This repo contains GGUF format model files for [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-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).

## Prompt template

```
<|system|>
{system_prompt}<|end|>
<|user|>
{prompt}<|end|>
<|assistant|>
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Phi-3.5-mini-instruct-Q2_K.gguf](https://huggingface.co/tensorblock/Phi-3.5-mini-instruct-GGUF/tree/main/Phi-3.5-mini-instruct-Q2_K.gguf) | Q2_K | 1.348 GB | smallest, significant quality loss - not recommended for most purposes |
| [Phi-3.5-mini-instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/Phi-3.5-mini-instruct-GGUF/tree/main/Phi-3.5-mini-instruct-Q3_K_S.gguf) | Q3_K_S | 1.566 GB | very small, high quality loss |
| [Phi-3.5-mini-instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/Phi-3.5-mini-instruct-GGUF/tree/main/Phi-3.5-mini-instruct-Q3_K_M.gguf) | Q3_K_M | 1.749 GB | very small, high quality loss |
| [Phi-3.5-mini-instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/Phi-3.5-mini-instruct-GGUF/tree/main/Phi-3.5-mini-instruct-Q3_K_L.gguf) | Q3_K_L | 1.905 GB | small, substantial quality loss |
| [Phi-3.5-mini-instruct-Q4_0.gguf](https://huggingface.co/tensorblock/Phi-3.5-mini-instruct-GGUF/tree/main/Phi-3.5-mini-instruct-Q4_0.gguf) | Q4_0 | 2.027 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Phi-3.5-mini-instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/Phi-3.5-mini-instruct-GGUF/tree/main/Phi-3.5-mini-instruct-Q4_K_S.gguf) | Q4_K_S | 2.043 GB | small, greater quality loss |
| [Phi-3.5-mini-instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/Phi-3.5-mini-instruct-GGUF/tree/main/Phi-3.5-mini-instruct-Q4_K_M.gguf) | Q4_K_M | 2.160 GB | medium, balanced quality - recommended |
| [Phi-3.5-mini-instruct-Q5_0.gguf](https://huggingface.co/tensorblock/Phi-3.5-mini-instruct-GGUF/tree/main/Phi-3.5-mini-instruct-Q5_0.gguf) | Q5_0 | 2.460 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Phi-3.5-mini-instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/Phi-3.5-mini-instruct-GGUF/tree/main/Phi-3.5-mini-instruct-Q5_K_S.gguf) | Q5_K_S | 2.460 GB | large, low quality loss - recommended |
| [Phi-3.5-mini-instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/Phi-3.5-mini-instruct-GGUF/tree/main/Phi-3.5-mini-instruct-Q5_K_M.gguf) | Q5_K_M | 2.529 GB | large, very low quality loss - recommended |
| [Phi-3.5-mini-instruct-Q6_K.gguf](https://huggingface.co/tensorblock/Phi-3.5-mini-instruct-GGUF/tree/main/Phi-3.5-mini-instruct-Q6_K.gguf) | Q6_K | 2.920 GB | very large, extremely low quality loss |
| [Phi-3.5-mini-instruct-Q8_0.gguf](https://huggingface.co/tensorblock/Phi-3.5-mini-instruct-GGUF/tree/main/Phi-3.5-mini-instruct-Q8_0.gguf) | Q8_0 | 3.782 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/Phi-3.5-mini-instruct-GGUF --include "Phi-3.5-mini-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/Phi-3.5-mini-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```