Instructions to use SamahFodeh/carecomm-tabpo-70b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SamahFodeh/carecomm-tabpo-70b-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("lm2445/TABPO_llama3.3_70B_3epoch") model = PeftModel.from_pretrained(base_model, "SamahFodeh/carecomm-tabpo-70b-lora") - Notebooks
- Google Colab
- Kaggle
CareComm TAB-PO 70B LoRA
LoRA adapter for CareComm evaluation / annotation (not the conversational patient agent).
- Base model: lm2445/TABPO_llama3.3_70B_3epoch
- Task: CareComm category annotation / debrief scoring
- Training data: ACI-Bench encounters labeled with CareComm schema
Load
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = "lm2445/TABPO_llama3.3_70B_3epoch"
adapter = "SamahFodeh/carecomm-tabpo-70b-lora"
tokenizer = AutoTokenizer.from_pretrained(adapter)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)
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Base model
lm2445/TABPO_llama3.3_70B_3epoch