metadata
license: llama3
language:
- en
library_name: peft
pipeline_tag: text-generation
base_model: vineetdaniels/NYXMed-V17-Merged
tags:
- medical
- radiology
- medical-coding
- icd-10
- cpt
- llama-3
- lora
- peft
- healthcare
NYXMed V18 — Radiology Coding LoRA Adapter
LoRA adapter trained on top of vineetdaniels/NYXMed-V17-Merged,
targeting primary-ICD accuracy with proximity-ranked retrieval candidates.
For a deployable single model, use vineetdaniels/NYXMed-V18-Merged.
Highlights
- Best eval_loss: 0.0710 (early-stopped at step 1,700; best checkpoint step 1,400)
- Trained on 59,170 coder-verified examples weighted toward primary-ICD corrections (family-swaps 47%)
- Built on the proximity-ranking retrieval fix (+21.5pp recall@10 of the correct primary on previously-wrong records) — must be deployed with the matching preprocessor change
Training
| Base | vineetdaniels/NYXMed-V17-Merged |
| LoRA | r=64, α=128, dropout=0.05, targets q/k/v/o/gate/up/down_proj |
| Examples | 59,170 (weighted) |
| Effective batch | 32 |
| Hardware | 4× H200, ~10.9h |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
base = AutoModelForCausalLM.from_pretrained("vineetdaniels/NYXMed-V17-Merged", torch_dtype=torch.bfloat16, device_map="auto")
tok = AutoTokenizer.from_pretrained("vineetdaniels/NYXMed-V18-Model")
model = PeftModel.from_pretrained(base, "vineetdaniels/NYXMed-V18-Model").eval()
eval_loss is on V18's own held-out split (not directly comparable to V17's split). The authoritative metric is primary-ICD accuracy on a common held-out production set. Radiology-only, review-then-accept use.