Instructions to use microsoft/Phi-3.5-vision-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/Phi-3.5-vision-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="microsoft/Phi-3.5-vision-instruct", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-vision-instruct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use microsoft/Phi-3.5-vision-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "microsoft/Phi-3.5-vision-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-3.5-vision-instruct", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/microsoft/Phi-3.5-vision-instruct
- SGLang
How to use microsoft/Phi-3.5-vision-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-3.5-vision-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-3.5-vision-instruct", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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-3.5-vision-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-3.5-vision-instruct", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use microsoft/Phi-3.5-vision-instruct with Docker Model Runner:
docker model run hf.co/microsoft/Phi-3.5-vision-instruct
RuntimeError: shape mismatch: value tensor of shape [4706, 3072] cannot be broadcast to indexing result of shape [9412, 3072]
Hello. Sorry if the question was asked before.
I have been following the official finetuning https://github.com/microsoft/Phi-3CookBook/blob/main/code/04.Finetuning/vision_finetuning/finetune_hf_trainer_nlvr2.py.
Step by step. With my dataset, but I'm having this error.
ile ~/.cache/huggingface/modules/transformers_modules/microsoft/Phi-3.5-vision-instruct/4a0d683eba9f1d0cbfb6151705d1ee73c25a80ca/modeling_phi3_v.py:241, in Phi3ImageEmbedding.forward(self, input_ids, pixel_values, image_sizes)
237 img_features = self.get_img_features(pixel_values.flatten(0, 1)).reshape(
238 num_images, num_crops, -1, self.image_dim_out
239 )
240 image_features_proj = self.hd_feature_transform(img_features, image_sizes)
--> 241 hidden_states = hidden_states.index_put(
242 positions, image_features_proj, accumulate=False
243 )
245 if self.drop is not None:
246 hidden_states = self.drop(hidden_states)
RuntimeError: shape mismatch: value tensor of shape [4706, 3072] cannot be broadcast to indexing result of shape [9412, 3072]
After days trying to understand the error, I still don't understand. Noted that have been able to train using PEFT without any problem.
Please any assistance will help
Thanks