Instructions to use OpenFace-CQUPT/Human_LLaVA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenFace-CQUPT/Human_LLaVA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="OpenFace-CQUPT/Human_LLaVA")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("OpenFace-CQUPT/Human_LLaVA") model = AutoModelForImageTextToText.from_pretrained("OpenFace-CQUPT/Human_LLaVA") - Notebooks
- Google Colab
- Kaggle
File size: 1,208 Bytes
babae27 | 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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | {
"architectures": [
"LlavaForConditionalGeneration"
],
"ignore_index": -100,
"image_token_index": 128257,
"model_type": "llava",
"pad_token_id": 128258,
"projector_hidden_act": "gelu",
"text_config": {
"_name_or_path": "./iter_45000_ft/llm_merge_lora",
"architectures": [
"LlamaForCausalLM"
],
"bos_token_id": 128000,
"eos_token_id": 128009,
"intermediate_size": 14336,
"max_position_embeddings": 8192,
"model_type": "llama",
"num_key_value_heads": 8,
"rms_norm_eps": 1e-05,
"rope_theta": 500000.0,
"torch_dtype": "float16",
"vocab_size": 128320
},
"torch_dtype": "float16",
"transformers_version": "4.43.3",
"vision_config": {
"_name_or_path": "./iter_45000_ft/vit_merge_lora",
"architectures": [
"SiglipVisionModel"
],
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 1152,
"image_size": 384,
"intermediate_size": 4304,
"layer_norm_eps": 1e-06,
"model_type": "siglip_vision_model",
"num_attention_heads": 16,
"num_hidden_layers": 27,
"patch_size": 14,
"torch_dtype": "float32"
},
"vision_feature_layer": -2,
"vision_feature_select_strategy": "default"
}
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