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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload config.json with huggingface_hub
Browse files- config.json +113 -0
config.json
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{
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"architectures": [
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"MiniCPMV"
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],
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_minicpm.MiniCPMVConfig",
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"AutoModel": "modeling_minicpmv.MiniCPMV",
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"AutoModelForCausalLM": "modeling_minicpmv.MiniCPMV"
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},
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"batch_vision_input": true,
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"bos_token_id": 151643,
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"drop_vision_last_layer": false,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"gene_config": {
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"assay": true,
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"batch_first": true,
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"cls_classes": 164,
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"context_length": 1500,
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"dim_feedforward": 1024,
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"dim_model": 512,
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"dropout": 0.0,
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"learnable_pe": true,
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"masking_p": 0.15,
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"modality": true,
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"model_type": "nicheformer",
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"n_tokens": 20340,
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"nheads": 16,
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"nlayers": 12,
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"specie": true,
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"supervised_task": null
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},
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"gene_hidden_size": 512,
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"gene_max_length": 1500,
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"gene_vocab_size": 1536,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"image_size": 448,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "minicpmv",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"patch_size": 14,
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"query_num": 64,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000.0,
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"slice_config": {
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"max_slice_nums": 9,
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"model_type": "minicpmv",
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"patch_size": 14,
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"scale_resolution": 448
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},
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"slice_mode": true,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "4.57.1",
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"use_cache": true,
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"use_gene_module": true,
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"use_image_id": true,
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"use_sliding_window": false,
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"version": 2.6,
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"vision_batch_size": 16,
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"vision_config": {
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"attention_dropout": 0.0,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_size": 1152,
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"image_size": 980,
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"intermediate_size": 4304,
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"layer_norm_eps": 1e-06,
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"model_type": "siglip_vision_model",
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"num_attention_heads": 16,
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"num_channels": 3,
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"num_hidden_layers": 27,
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"patch_size": 14
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},
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"vocab_size": 151666
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}
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