---
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE
language:
- multilingual
pipeline_tag: text-generation
tags:
- nlp
- code
- TensorBlock
- GGUF
inference:
  parameters:
    temperature: 0.7
widget:
- messages:
  - role: user
    content: Can you provide ways to eat combinations of bananas and dragonfruits?
base_model: microsoft/Phi-3-medium-4k-instruct
---

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## microsoft/Phi-3-medium-4k-instruct - GGUF

This repo contains GGUF format model files for [microsoft/Phi-3-medium-4k-instruct](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit ec7f3ac](https://github.com/ggerganov/llama.cpp/commit/ec7f3ac9ab33e46b136eb5ab6a76c4d81f57c7f1).

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## Prompt template

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

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Phi-3-medium-4k-instruct-Q2_K.gguf](https://huggingface.co/tensorblock/Phi-3-medium-4k-instruct-GGUF/blob/main/Phi-3-medium-4k-instruct-Q2_K.gguf) | Q2_K | 5.143 GB | smallest, significant quality loss - not recommended for most purposes |
| [Phi-3-medium-4k-instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/Phi-3-medium-4k-instruct-GGUF/blob/main/Phi-3-medium-4k-instruct-Q3_K_S.gguf) | Q3_K_S | 6.065 GB | very small, high quality loss |
| [Phi-3-medium-4k-instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/Phi-3-medium-4k-instruct-GGUF/blob/main/Phi-3-medium-4k-instruct-Q3_K_M.gguf) | Q3_K_M | 6.923 GB | very small, high quality loss |
| [Phi-3-medium-4k-instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/Phi-3-medium-4k-instruct-GGUF/blob/main/Phi-3-medium-4k-instruct-Q3_K_L.gguf) | Q3_K_L | 7.490 GB | small, substantial quality loss |
| [Phi-3-medium-4k-instruct-Q4_0.gguf](https://huggingface.co/tensorblock/Phi-3-medium-4k-instruct-GGUF/blob/main/Phi-3-medium-4k-instruct-Q4_0.gguf) | Q4_0 | 7.897 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Phi-3-medium-4k-instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/Phi-3-medium-4k-instruct-GGUF/blob/main/Phi-3-medium-4k-instruct-Q4_K_S.gguf) | Q4_K_S | 7.954 GB | small, greater quality loss |
| [Phi-3-medium-4k-instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/Phi-3-medium-4k-instruct-GGUF/blob/main/Phi-3-medium-4k-instruct-Q4_K_M.gguf) | Q4_K_M | 8.567 GB | medium, balanced quality - recommended |
| [Phi-3-medium-4k-instruct-Q5_0.gguf](https://huggingface.co/tensorblock/Phi-3-medium-4k-instruct-GGUF/blob/main/Phi-3-medium-4k-instruct-Q5_0.gguf) | Q5_0 | 9.622 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Phi-3-medium-4k-instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/Phi-3-medium-4k-instruct-GGUF/blob/main/Phi-3-medium-4k-instruct-Q5_K_S.gguf) | Q5_K_S | 9.622 GB | large, low quality loss - recommended |
| [Phi-3-medium-4k-instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/Phi-3-medium-4k-instruct-GGUF/blob/main/Phi-3-medium-4k-instruct-Q5_K_M.gguf) | Q5_K_M | 10.074 GB | large, very low quality loss - recommended |
| [Phi-3-medium-4k-instruct-Q6_K.gguf](https://huggingface.co/tensorblock/Phi-3-medium-4k-instruct-GGUF/blob/main/Phi-3-medium-4k-instruct-Q6_K.gguf) | Q6_K | 11.454 GB | very large, extremely low quality loss |
| [Phi-3-medium-4k-instruct-Q8_0.gguf](https://huggingface.co/tensorblock/Phi-3-medium-4k-instruct-GGUF/blob/main/Phi-3-medium-4k-instruct-Q8_0.gguf) | Q8_0 | 14.835 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-medium-4k-instruct-GGUF --include "Phi-3-medium-4k-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-medium-4k-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```