Visual Question Answering
Transformers
Safetensors
cvrr_merged
feature-extraction
cvrr
custom_code
latent-reasoning
Instructions to use dmis-lab/InternVL3-9B-CVRR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmis-lab/InternVL3-9B-CVRR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="dmis-lab/InternVL3-9B-CVRR", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dmis-lab/InternVL3-9B-CVRR", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 878 Bytes
a381a62 | 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 | {
"model_type": "cvrr_merged",
"architectures": [
"CVRRMergedModel"
],
"auto_map": {
"AutoConfig": "configuration_cvrr_merged.CVRRMergedConfig",
"AutoModel": "modeling_cvrr_merged.CVRRMergedModel",
"AutoModelForImageTextToText": "modeling_cvrr_merged.CVRRMergedModel"
},
"release": {
"name": "CVRR-InternVL3-9B",
"base_model": "OpenGVLab/InternVL3-9B",
"ell_star": 35,
"recurrent_layer": 36,
"upper_decoder_start": 37,
"inference_T": 4,
"inference_beta": 0.33,
"lora_rank": 32,
"lora_alpha": 12.0,
"lora_dropout": 0.01,
"checkpoint_step": 500,
"format": "cvrr_native_plus_merged_transition_v1",
"merged_weight_dtype": "float32",
"native_weight_bytes": 18277586944,
"status": "gpu_vstar_comparison_completed",
"upload_ready": true,
"hub_model_id": "dmis-lab/InternVL3-9B-CVRR"
}
}
|