Instructions to use microsoft/Phi-3-vision-128k-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Phi-3-vision-128k-instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="microsoft/Phi-3-vision-128k-instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-vision-128k-instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use microsoft/Phi-3-vision-128k-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/Phi-3-vision-128k-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Phi-3-vision-128k-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/microsoft/Phi-3-vision-128k-instruct

SGLang

How to use microsoft/Phi-3-vision-128k-instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "microsoft/Phi-3-vision-128k-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Phi-3-vision-128k-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "microsoft/Phi-3-vision-128k-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Phi-3-vision-128k-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use microsoft/Phi-3-vision-128k-instruct with Docker Model Runner:
```
docker model run hf.co/microsoft/Phi-3-vision-128k-instruct
```

LoRA Finetuning - Text vs Vision Effects

#54

by brecker - opened Jul 16, 2024

Discussion

brecker

Jul 16, 2024

•

edited Jul 16, 2024

2 questions:

Are you able to finetune only the textual aspect of Phi3V without finetuning the vision component?
-Say I want to 'retain' (I assume they will degrade with finetuning) the models vision capabilities while finetuning it for function calling etc. The multi-modality of the model is important to me
Which are the best target modules to target when performing LoRA FT on Phi3V?

2U1

Jul 17, 2024

•

edited Jul 17, 2024

Yes you could.

Actually when using LoRA the official code shows you to fine tune only the language _model part.

I'm not exactly sure, so I'm just performing all of the layers except for the "lm_head".

brecker

Jul 17, 2024

Thank you- @2U1 Can you point me to where you refer here: 'the official code shows you to fine tune only the language _model part'

2U1

Jul 17, 2024

•

edited Jul 17, 2024

@brecker
https://github.com/microsoft/Phi-3CookBook/blob/20d56d79cfd38eb175118ecc961a9b49e2341de2/code/04.Finetuning/vision_finetuning/finetune_hf_trainer_hateful_memes.py#L374-L384

here's the link for you.
However the img_projection layer is in the vision_model part so it freeze also.

If you need to controll all three part to freeze/unfreeze , you can use my code.
https://github.com/2U1/Phi3-Vision-ft

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