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
File size: 482 Bytes
7434286 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"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
}
|