Visual Question Answering
Transformers
Safetensors
English
phi4mm
text-generation
phi4
gptq
quantized
compressed-tensors
vision-language
audio
multimodal
vllm
custom_code
Instructions to use Swicked86/phi4-mm-gptq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Swicked86/phi4-mm-gptq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="Swicked86/phi4-mm-gptq", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Swicked86/phi4-mm-gptq", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "auto_map": { | |
| "AutoProcessor": "processing_phi4mm.Phi4MMProcessor", | |
| "AutoImageProcessor": "processing_phi4mm.Phi4MMImageProcessor", | |
| "AutoFeatureExtractor": "processing_phi4mm.Phi4MMAudioFeatureExtractor" | |
| }, | |
| "image_processor_type": "Phi4MMImageProcessor", | |
| "processor_class": "Phi4MMProcessor", | |
| "feature_extractor_type": "Phi4MMAudioFeatureExtractor", | |
| "audio_compression_rate": 8, | |
| "audio_downsample_rate": 1, | |
| "audio_feat_stride": 1, | |
| "dynamic_hd": 36 | |
| } | |