# Phi3-Vision Best Practice

## 目录
- [Environment Setup](#environment-setup)
- [Inference](#inference)
- [Fine-tuning](#fine-tuning)
- [Inference After Fine-tuning](#inference-after-fine-tuning)


## Environment Setup
```shell
git clone https://github.com/modelscope/swift.git
cd swift
pip install -e '.[llm]'
```

Model Link:
- phi3-vision-128k-instruct: [https://modelscope.cn/models/LLM-Research/Phi-3-vision-128k-instruct/summary](https://modelscope.cn/models/LLM-Research/Phi-3-vision-128k-instruct/summary)


## Inference

Inference with phi3-vision-128k-instruct:
```shell
# Experimental environment: A10, 3090, V100, ...
# 16GB GPU memory
CUDA_VISIBLE_DEVICES=0 swift infer --model_type phi3-vision-128k-instruct
```

Output: (supports passing local path or URL)
```python
"""
<<< Who are you?
I am Phi, an AI developed by Microsoft to assist with providing information, answering questions, and helping users find solutions to their queries. How can I assist you today?
--------------------------------------------------
<<< clear
<<< <img>http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/animal.png</img><img>http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/cat.png</img>What is the difference between these two pictures?
The first picture shows a group of four cartoon sheep standing in a field, while the second picture is a close-up of a kitten with a blurred background. The main difference between these two pictures is the subject matter and the setting. The first picture features animals that are typically associated with farm life and agriculture, while the second picture focuses on a domestic animal, a kitten, which is more commonly found in households. Additionally, the first picture has a more peaceful and serene atmosphere, while the second picture has a more intimate and detailed view of the kitten.
--------------------------------------------------
<<< clear
<<< <img>http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/animal.png</img>How many sheep are there in the picture?
There are four sheep in the picture.
--------------------------------------------------
<<< clear
<<< <img>http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/math.png</img>What is the result of the calculation?
The result of the calculation 1452 + 45304 is 46756.
--------------------------------------------------
<<< clear
<<< <img>http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/poem.png</img>Write a poem based on the content of the picture.
In the tranquil night, a boat sails,
Through the darkened river, it sets sail.
A single candle flickers, casting light,
Guiding the way through the endless night.

The stars above, like diamonds bright,
Gleam down upon the boat's gentle flight.
The moon, a silent guardian in the sky,
Watches over the boat as it sails by.

The river, a mirror to the night,
Reflects the boat's journey, a beautiful sight.
The trees on either side, standing tall,
Whisper secrets to the boat, one and all.

In the stillness of the night, a sense of peace,
The boat, the river, the trees, all in their place.
A moment frozen in time, a scene so serene,
A journey through the night, a dream so unseen.
"""
```

Sample images is as follows:

cat:

<img src="http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/cat.png" width="250" style="display: inline-block;">

animal:

<img src="http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/animal.png" width="250" style="display: inline-block;">

math:

<img src="http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/math.png" width="250" style="display: inline-block;">

poem:

<img src="http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/poem.png" width="250" style="display: inline-block;">


**Single-sample Inference**

```python
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

from swift.llm import (
    get_model_tokenizer, get_template, inference, ModelType,
    get_default_template_type, inference_stream
)
from swift.utils import seed_everything
import torch

model_type = ModelType.phi3_vision_128k_instruct
template_type = get_default_template_type(model_type)
print(f'template_type: {template_type}')

model, tokenizer = get_model_tokenizer(model_type, torch.float16,
                                       model_kwargs={'device_map': 'auto'})
model.generation_config.max_new_tokens = 256
template = get_template(template_type, tokenizer)
seed_everything(42)

query = """<img>http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/road.png</img>How far is it from each city?"""
response, history = inference(model, template, query)
print(f'query: {query}')
print(f'response: {response}')

# Streaming
query = 'Which city is the farthest?'
gen = inference_stream(model, template, query, history)
print_idx = 0
print(f'query: {query}\nresponse: ', end='')
for response, history in gen:
    delta = response[print_idx:]
    print(delta, end='', flush=True)
    print_idx = len(response)
print()
print(f'history: {history}')
"""
query: Which city is the farthest?
response: Guangzhou is the farthest city, located 293km away.
history: [['<img>http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/road.png</img>How far is it from each city?', 'The distances are as follows: Mata is 14km away, Yangjiang is 62km away, and Guangzhou is 293km away.'], ['Which city is the farthest?', 'Guangzhou is the farthest city, located 293km away.']]
"""
```

Sample image is as follows:

road:

<img src="http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/road.png" width="250" style="display: inline-block;">


## Fine-tuning
Multimodal large model fine-tuning usually uses **custom datasets**. Here is a demo that can be run directly:

(Default fine-tune only the LLM part of qkv with lora. If you want to fine-tune all linear modules containing vision model parts, you can specify `--lora_target_modules ALL`. Support to fine-tune all parameters.)
```shell
# Experimental environment: A10, 3090, V100, ...
# 16GB GPU memory
CUDA_VISIBLE_DEVICES=0 swift sft \
    --model_type phi3-vision-128k-instruct \
    --dataset coco-en-mini \
```

[Custom datasets](../LLM/Customization.md#-Recommended-Command-line-arguments) supports json, jsonl styles. The following is an example of a custom dataset:

(Supports multi-turn dialogue, support for multi-image or non-image per turn, supports input of local path or URL.)

```json
[
    {"conversations": [
        {"from": "user", "value": "<img>img_path</img>11111"},
        {"from": "assistant", "value": "22222"}
    ]},
    {"conversations": [
        {"from": "user", "value": "<img>img_path</img><img>img_path2</img><img>img_path3</img>aaaaa"},
        {"from": "assistant", "value": "bbbbb"},
        {"from": "user", "value": "<img>img_path</img>ccccc"},
        {"from": "assistant", "value": "ddddd"}
    ]},
    {"conversations": [
        {"from": "user", "value": "AAAAA"},
        {"from": "assistant", "value": "BBBBB"},
        {"from": "user", "value": "CCCCC"},
        {"from": "assistant", "value": "DDDDD"}
    ]}
]
```


## Inference After Fine-tuning
Direct inference:
```shell
CUDA_VISIBLE_DEVICES=0 swift infer \
    --ckpt_dir output/phi3-vision-128k-instruct/vx-xxx/checkpoint-xxx \
    --load_dataset_config true \
```

**merge-lora** and infer:
```shell
CUDA_VISIBLE_DEVICES=0 swift export \
    --ckpt_dir output/phi3-vision-128k-instruct/vx-xxx/checkpoint-xxx \
    --merge_lora true --safe_serialization false

CUDA_VISIBLE_DEVICES=0 swift infer \
    --ckpt_dir output/phi3-vision-128k-instruct/vx-xxx/checkpoint-xxx-merged \
    --load_dataset_config true
```