gguf nlp code text-generation en base_model:microsoft/Phi-3-mini-4k-instruct base_model:quantized:microsoft/Phi-3-mini-4k-instruct license:mit region:us conversational
---
base_model: microsoft/Phi-3-mini-4k-instruct
language:
- en
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE
model_name: Phi-3-mini-4k-instruct
pipeline_tag: text-generation
tags:
- nlp
- code
inference: false
model_creator: microsoft
model_type: phi3
quantized_by: brittlewis12
---

# Phi 3 Mini 4K Instruct GGUF

***Updated with Microsoft’s [latest model changes](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/commit/4f818b18e097c9ae8f93a29a57027cad54b75304) as of July 21, 2024***

**Original model**: [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)

**Model creator**: [Microsoft](https://huggingface.co/microsoft)

This repo contains GGUF format model files for Microsoft’s Phi 3 Mini 4K Instruct.

> The Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties.

Learn more on Microsoft’s [Model page](https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/).

### What is GGUF?

GGUF is a file format for representing AI models. It is the third version of the format, 
introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. 
Converted with llama.cpp build 3432 (revision [45f2c19](https://github.com/ggerganov/llama.cpp/commit/45f2c19cc57286eead7b232ce8028273a817aa4d)),
using [autogguf](https://github.com/brittlewis12/autogguf).

### Prompt template

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

```

---

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---

## Original Model Evaluation

Comparison of July update vs original April release:

| Benchmarks | Original | June 2024 Update |
|------------|----------|------------------|
| Instruction Extra Hard | 5.7 | 6.0 |
| Instruction Hard | 4.9 | 5.1 |
| Instructions Challenge | 24.6 | 42.3 |
| JSON Structure Output | 11.5 | 52.3 |
| XML Structure Output | 14.4 | 49.8 |
| GPQA	| 23.7	| 30.6 |
| MMLU	| 68.8	| 70.9 |
| **Average**	| **21.9**	| **36.7** |


---

### Original April release

> As is now standard, we use few-shot prompts to evaluate the models, at temperature 0. 
> The prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3.
> More specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model.
> 
> The number of k–shot examples is listed per-benchmark. 

|   | Phi-3-Mini-4K-In<br>3.8b | Phi-2<br>2.7b | Mistral<br>7b | Gemma<br>7b | Llama-3-In<br>8b | Mixtral<br>8x7b | GPT-3.5<br>version 1106 |
|---|---|---|---|---|---|---|---|
| MMLU <br>5-Shot | 68.8 | 56.3 | 61.7 | 63.6 | 66.5 | 68.4 | 71.4 |
| HellaSwag <br> 5-Shot | 76.7 | 53.6 | 58.5 | 49.8 | 71.1 | 70.4 | 78.8 |
| ANLI <br> 7-Shot | 52.8 | 42.5 | 47.1 | 48.7 | 57.3 | 55.2 | 58.1 |
| GSM-8K <br> 0-Shot; CoT | 82.5 | 61.1 | 46.4 | 59.8 | 77.4 | 64.7 | 78.1 |
| MedQA <br> 2-Shot | 53.8 | 40.9 | 49.6 | 50.0 | 60.5 | 62.2 | 63.4 |
| AGIEval <br> 0-Shot | 37.5 | 29.8 | 35.1 | 42.1 | 42.0 | 45.2 | 48.4 |
| TriviaQA <br> 5-Shot | 64.0 | 45.2 | 72.3 | 75.2 | 67.7 | 82.2 | 85.8 |
| Arc-C <br> 10-Shot | 84.9 | 75.9 | 78.6 | 78.3 | 82.8 | 87.3 | 87.4 |
| Arc-E <br> 10-Shot | 94.6 | 88.5 | 90.6 | 91.4 | 93.4 | 95.6 | 96.3 |
| PIQA <br> 5-Shot | 84.2 | 60.2 | 77.7 | 78.1 | 75.7 | 86.0 | 86.6 |
| SociQA <br> 5-Shot | 76.6 | 68.3 | 74.6 | 65.5 | 73.9 | 75.9 | 68.3 |
| BigBench-Hard <br> 0-Shot | 71.7 | 59.4 | 57.3 | 59.6 | 51.5 | 69.7 | 68.32 |
| WinoGrande <br> 5-Shot | 70.8 | 54.7 | 54.2 | 55.6 | 65 | 62.0 | 68.8 |
| OpenBookQA <br> 10-Shot | 83.2 | 73.6 | 79.8 | 78.6 | 82.6 | 85.8 | 86.0 |
| BoolQ <br> 0-Shot | 77.6 | -- | 72.2 | 66.0 | 80.9 | 77.6 | 79.1 |
| CommonSenseQA <br> 10-Shot | 80.2 | 69.3 | 72.6 | 76.2 | 79 | 78.1 | 79.6 |
| TruthfulQA <br> 10-Shot | 65.0 | -- | 52.1 | 53.0 | 63.2 | 60.1 | 85.8 |
| HumanEval <br> 0-Shot | 59.1 | 47.0 | 28.0 | 34.1 | 60.4 | 37.8 | 62.2 |
| MBPP <br> 3-Shot | 53.8 | 60.6 | 50.8 | 51.5 | 67.7 | 60.2 | 77.8 |
