Instructions to use 10ths/FADS_base_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 10ths/FADS_base_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="10ths/FADS_base_model", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForVision2Seq model = AutoModelForVision2Seq.from_pretrained("10ths/FADS_base_model", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 873 Bytes
790e11e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"_name_or_path": "Salesforce/xgen-mm-phi3-mini-instruct-r-v1",
"architectures": [
"XGenMMModelForConditionalGeneration"
],
"auto_map": {
"AutoConfig": "Salesforce/xgen-mm-phi3-mini-instruct-r-v1--configuration_xgenmm.XGenMMConfig",
"AutoModelForVision2Seq": "Salesforce/xgen-mm-phi3-mini-instruct-r-v1--modeling_xgenmm.XGenMMModelForConditionalGeneration"
},
"model_type": "xgenmm",
"text_config": {
"initial_tokenizer_len": 32012,
"model_type": "phi3",
"sliding_window": 2047,
"torch_dtype": "bfloat16"
},
"torch_dtype": "bfloat16",
"transformers_version": "4.41.2",
"vision_encoder_config": {
"anyres_patch_sampling": true,
"image_aspect_ratio": "anyres",
"model_type": "xgenmm_vision_encoder"
},
"vision_tokenizer_config": {
"model_type": "xgenmm_vision_tokenizer"
}
}
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