Feature Extraction
Transformers
Safetensors
English
Chinese
minicpmv
histopathology
multimodal
spatial-transcriptomics
custom_code
Instructions to use openbmb/SciCore-Omics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/SciCore-Omics with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="openbmb/SciCore-Omics", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/SciCore-Omics", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "MiniCPMV" | |
| ], | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_minicpm.MiniCPMVConfig", | |
| "AutoModel": "modeling_minicpmv.MiniCPMV", | |
| "AutoModelForCausalLM": "modeling_minicpmv.MiniCPMV" | |
| }, | |
| "batch_vision_input": true, | |
| "bos_token_id": 151643, | |
| "drop_vision_last_layer": false, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 151645, | |
| "gene_config": { | |
| "assay": true, | |
| "batch_first": true, | |
| "cls_classes": 164, | |
| "context_length": 1500, | |
| "dim_feedforward": 1024, | |
| "dim_model": 512, | |
| "dropout": 0.0, | |
| "learnable_pe": true, | |
| "masking_p": 0.15, | |
| "modality": true, | |
| "model_type": "nicheformer", | |
| "n_tokens": 20340, | |
| "nheads": 16, | |
| "nlayers": 12, | |
| "specie": true, | |
| "supervised_task": null | |
| }, | |
| "gene_hidden_size": 512, | |
| "gene_max_length": 1500, | |
| "gene_vocab_size": 1536, | |
| "hidden_act": "silu", | |
| "hidden_size": 3584, | |
| "image_size": 448, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 18944, | |
| "layer_types": [ | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention" | |
| ], | |
| "max_position_embeddings": 32768, | |
| "max_window_layers": 28, | |
| "model_type": "minicpmv", | |
| "num_attention_heads": 28, | |
| "num_hidden_layers": 28, | |
| "num_key_value_heads": 4, | |
| "patch_size": 14, | |
| "query_num": 64, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "rope_theta": 1000000.0, | |
| "slice_config": { | |
| "max_slice_nums": 9, | |
| "model_type": "minicpmv", | |
| "patch_size": 14, | |
| "scale_resolution": 448 | |
| }, | |
| "slice_mode": true, | |
| "sliding_window": null, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "4.57.1", | |
| "use_cache": true, | |
| "use_gene_module": true, | |
| "use_image_id": true, | |
| "use_sliding_window": false, | |
| "version": 2.6, | |
| "vision_batch_size": 16, | |
| "vision_config": { | |
| "attention_dropout": 0.0, | |
| "hidden_act": "gelu_pytorch_tanh", | |
| "hidden_size": 1152, | |
| "image_size": 980, | |
| "intermediate_size": 4304, | |
| "layer_norm_eps": 1e-06, | |
| "model_type": "siglip_vision_model", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 27, | |
| "patch_size": 14 | |
| }, | |
| "vocab_size": 151666 | |
| } | |