Text Classification
Transformers
Safetensors
PEFT
branchone
feature-extraction
decision-model
multimodal
custom-code
custom_code
Instructions to use ndemirdemir/BranchOne with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ndemirdemir/BranchOne with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ndemirdemir/BranchOne", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ndemirdemir/BranchOne", trust_remote_code=True, device_map="auto") - PEFT
How to use ndemirdemir/BranchOne with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Download model_config.json from ndemirdemir/BranchOne: direct link, hf CLI and curl.
- Browser
- Download file 367 Bytes
-
https://huggingface.co/ndemirdemir/BranchOne/resolve/main/model_config.json
- Command line
-
hf download hf://ndemirdemir/BranchOne/model_config.json
-
curl -L -o model_config.json https://huggingface.co/ndemirdemir/BranchOne/resolve/main/model_config.json
367 Bytes
| { | |
| "model_id": "Qwen/Qwen3.5-0.8B-Base", | |
| "revision": "dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68", | |
| "max_length": 8192, | |
| "image_tokens": 256, | |
| "max_rows": 1, | |
| "batch_token_budget": 8192, | |
| "lora_rank": 8, | |
| "prompt_version": "branchone-multimodal-segments-v2", | |
| "temperature": 1.0, | |
| "calibration_status": "unfitted-after-training", | |
| "cache_validated": false | |
| } |