Instructions to use nvidia/Flora-TempSeg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nvidia/Flora-TempSeg with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B") model = PeftModel.from_pretrained(base_model, "nvidia/Flora-TempSeg") - Notebooks
- Google Colab
- Kaggle
File size: 542 Bytes
1d74d27 143ed82 1d74d27 143ed82 1d74d27 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"schema_version": 2,
"model": "Flora-segmenter-9B",
"selected_step": 1300,
"base_model": "Qwen/Qwen3.5-9B",
"base_revision": "c202236235762e1c871ad0ccb60c8ee5ba337b9a",
"format": "PEFT LoRA",
"adapter_sha256": "1d80fe544e4cd1a7ff6b0ea4be71e988972408dab81d4c8af72e3a628739d6c3",
"adapter_tensor_count": 496,
"adapter_dtype": "F32",
"lora_rank": 32,
"lora_alpha": 64,
"lora_scope": "language-model attention and MLP projections",
"source_weights_modified": false,
"adapter_config_public_base_binding_only": true
}
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