---
license: llama3
language:
- en
library_name: peft
pipeline_tag: text-generation
base_model: vineetdaniels/NYXMed-V16-Model
tags:
- medical
- radiology
- medical-coding
- icd-10
- cpt
- llama-3
- llama-3-70b
- lora
- peft
- healthcare
- clinical
---

# NYXMed V17 — Radiology Medical Coding LLM

<p align="center">
  <em>Llama-3-70B fine-tune for autonomous CPT, ICD-10, and modifier coding from radiology reports.</em>
</p>

<p align="center">
  <a href="https://huggingface.co/vineetdaniels/NYXMed-V16-Model">V16 (base)</a> ·
  <a href="https://huggingface.co/vineetdaniels/NYXMed-V17-Model">V17 (this model)</a> ·
  <a href="https://huggingface.co/vineetdaniels/NYXMed-V17-Epoch1">V17 Epoch-1 (frozen checkpoint)</a>
</p>

---

## TL;DR

V17 is a **LoRA adapter** trained on top of [`vineetdaniels/NYXMed-V16-Model`](https://huggingface.co/vineetdaniels/NYXMed-V16-Model) (a Llama-3-70B fine-tune). It was trained on **113,032 coder-reviewed radiology cases** with a focus on raising ICD-10 accuracy without regressing CPT or modifier performance.

| Metric | V16 (base) | **V17 (this)** | Δ |
|---|---|---|---|
| **CPT exact match** | ~85% | **90.6%** | **+5.6 pts** |
| **Modifier exact match** | ~95% | **97.0%** | +2.0 pts |
| **Mean ICD recall** | ~65% | **83.4%** | **+18.4 pts** |
| **Final eval_loss** | ~0.25 | **0.0824** | **−67%** |
| Train examples | ~67K | **113,032** | +69% |
| Adds Exam Description + Reason | ❌ | ✅ | — |

V17 was trained to push **ICD recall above 80%** without regressing CPT — both goals achieved. Full metric breakdown in **Evaluation** below.

---

## What V17 is for

V17 takes a radiology report and outputs the billing codes a human coder would assign:

```
Input:  Exam description, reason for exam, full report text, and
        (optionally) retrieval-augmented examples + candidate codes.

Output: CPT[, CPT2], MOD, ICD1, ICD2, ICD3, ...
        e.g.  93970, 26, M79.89, I83.93
```

It is designed to be the LLM core inside an **autonomous coding pipeline** with retrieval (RAG), post-processing rules, and audit feedback loops — not as a standalone end-user model.

### Targets the model predicts

- **CPT-4** procedure codes (supports multi-code outputs)
- **Modifier-26 / TC / LT / RT / 50 / 59 / …**
- **ICD-10-CM** diagnosis codes (multi-label, ordered by clinical priority)

---

## Evaluation

Internal evaluation is performed against the live coder-reviewed Supabase dataset using a held-out validation split of **5,950 records**.

### Training-time eval_loss curve (held-out 250-sample slice)

| Epoch | Step | eval_loss |
|---|---|---|
| 0.03 | 100 | (≈ baseline) |
| 0.42 | 1,500 | 0.144 |
| 0.85 | 3,000 | 0.103 |
| 0.99 | 3,500 | 0.0875 |
| **1.02** | **3,600 ← best** | **0.0824** |
| 1.10 | 3,900 (stopped) | 0.0841 |

Early stopping triggered at step 3,900 (1.1 epochs); `load_best_model_at_end=True` reverted to the step-3,600 checkpoint.

### Domain-specific accuracy

Measured on **n = 500** randomly sampled held-out radiology reports (greedy decoding, batch=4, 4×H200):

| Metric | V17 |
|---|---|
| **CPT exact match** | **90.60%** |
| Primary CPT match | 91.40% |
| **Modifier exact match** | **97.00%** |
| **ICD-10 exact match** (full set) | 69.60% |
| ICD-10 any-overlap | 90.40% |
| ICD-10 root-overlap (`A99.x`-level) | 92.20% |
| **Mean ICD recall** | **83.37%** |
| Mean ICD precision | 85.05% |
| All-three exact (CPT + MOD + full ICD set) | 64.00% |

V17's primary training objective — **raise ICD recall above 80%** — was met (83.37%) while CPT (90.6%) and Modifier (97.0%) far exceeded the no-regression floor. Code-set-overlap metrics show V17 is identifying the correct *family* of ICD codes 92% of the time, with most remaining errors being specificity refinements (e.g. predicting `M25.5` instead of `M25.511`) rather than wrong-diagnosis errors.

---

## How to use

V17 is published as a **LoRA adapter**. You need the V16 base model alongside it.

### Option A — Transformers + PEFT

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

BASE = "vineetdaniels/NYXMed-V16-Model"
ADAPTER = "vineetdaniels/NYXMed-V17-Model"

tokenizer = AutoTokenizer.from_pretrained(ADAPTER, use_fast=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

base = AutoModelForCausalLM.from_pretrained(
    BASE,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="sdpa",
)
model = PeftModel.from_pretrained(base, ADAPTER)
model.eval()

messages = [
    {"role": "system", "content": "You are an expert radiology coder specializing in ICD-10 and CPT coding for radiology reports.\n\nFollow the coding rules provided in each request carefully."},
    {"role": "user", "content": "<your prompt with few-shot examples + report>"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.inference_mode():
    out = model.generate(**inputs, max_new_tokens=64, do_sample=False)
print(tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

### Option B — Merge & deploy with vLLM

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

base = AutoModelForCausalLM.from_pretrained("vineetdaniels/NYXMed-V16-Model", torch_dtype=torch.bfloat16)
merged = PeftModel.from_pretrained(base, "vineetdaniels/NYXMed-V17-Model").merge_and_unload()
merged.save_pretrained("./nyxmed-v17-merged")
AutoTokenizer.from_pretrained("vineetdaniels/NYXMed-V17-Model").save_pretrained("./nyxmed-v17-merged")
```

Then serve with vLLM:

```bash
vllm serve ./nyxmed-v17-merged \
    --dtype bfloat16 \
    --tensor-parallel-size 4 \
    --max-model-len 4096
```

### Generation settings (recommended)

| Param | Value |
|---|---|
| `do_sample` | `False` (greedy) |
| `max_new_tokens` | `64` |
| `temperature` | n/a |
| `top_p` | n/a |

Greedy decoding gives the most reproducible coding output. The model is robust enough that sampling rarely helps.

---

## Prompt format

V17 expects the Llama-3 chat template. The user message should contain (in order):

1. **Few-shot examples** retrieved by RAG (BM25 + FAISS + reranker)
2. **CPT candidate list** (top-K from RAG, ordered)
3. **ICD-10 candidate list** (top-K from RAG, ordered)
4. **Coding rules** (project-specific guardrails)
5. **The actual report**, in one of two formats (V17 was trained on both, ~70/30 split):
   - **Explicit**: separate `Exam Description:` and `Reason for Exam:` lines, then the body.
   - **Embedded**: report text only, with description/indication inline as in the source.

The expected assistant output is a single line:

```
<CPT>[ <CPT2> ...], <MOD>, <ICD1>, <ICD2>, ...
```

Empty modifier slot is allowed (e.g. `74176, , R10.84`).

---

## Training details

| Setting | Value |
|---|---|
| **Base model** | `vineetdaniels/NYXMed-V16-Model` (Llama-3-70B-Instruct fine-tune) |
| **Method** | LoRA with DeepSpeed ZeRO-3 |
| **LoRA rank (`r`)** | 64 |
| **LoRA alpha** | 128 |
| **LoRA dropout** | 0.05 |
| **Target modules** | `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj` |
| **Trainable params** | ~417 M of ~70.9 B (0.59%) |
| **Train examples** | 113,032 |
| **Validation examples** | 250 (sampled from 5,950-record held-out pool) |
| **Sequence length** | 2,560 tokens |
| **Effective batch size** | 32 (per-device 1 × grad accum 8 × 4 GPUs) |
| **Optimizer** | AdamW + DeepSpeed ZeRO-3 |
| **Learning rate** | 1e-5 (cosine schedule, 3% warmup) |
| **Epochs** | 2 (early stopped at 1.10) |
| **Total steps** | 3,900 |
| **Best step** | 3,600 (loaded back via `load_best_model_at_end`) |
| **Attention impl.** | `sdpa` (PyTorch built-in Flash Attention 2) |
| **Precision** | bfloat16 |
| **Hardware** | 4 × NVIDIA H200 SXM 80GB |
| **Wall-clock runtime** | 16.95 hours |

### Data composition

| Source | Count | Notes |
|---|---|---|
| Supabase coder-reviewed cases | ~46,000 | Includes 30K+ new records collected after V16 |
| Specificity-correction pairs | ~5,000 | Unspecified → specific ICD upgrades, 3× weighted |
| Hard-case audit set | ~3,000 | Multi-code or modifier-heavy reports |
| V16-era retained set | ~59,000 | Filtered to exclude records V16 already trained on |

A 3-layer **self-leakage defense** (content hash + cosine similarity + metadata fingerprint) prevented any training record from retrieving itself as a few-shot example during prompt assembly. **108K candidate retrievals were blocked** by this filter during training-data preparation.

---

## Intended use & limitations

### Intended use
- Augmenting human radiology coders in a **review-then-accept** workflow.
- Pre-coding reports for a downstream audit / verification pipeline.
- Research on LLM-based medical coding.

### Out of scope
- Direct billing without human review.
- Non-radiology specialties (cardiology, pathology, etc.). The training data is radiology-only.
- ICD-10 codes outside the radiology-relevant subset are under-represented.

### Known limitations
- **Long reports** (> 2,560 tokens) are truncated during inference; performance on extreme outliers may degrade.
- **Rare CPT/ICD combinations** appear infrequently in training and remain harder cases.
- The model is **English-only**.
- Outputs are deterministic with greedy decoding but the model can still produce **hallucinated codes** — production deployment must include code-validity checks against the official CMS code sets.

### Bias & safety
This is a **clinical decision-support model**. It must not be used to make autonomous billing or treatment decisions without review by a credentialed coder or clinician. The training data is sourced from a single organization's coder-reviewed dataset and may carry institutional coding preferences.

---

## Recovery / Checkpoints

If a deployment ever needs to roll back, the following snapshots are available on the Hub:

| Checkpoint | Where | Notes |
|---|---|---|
| V17 final (best step 3,600) | this repo | `eval_loss = 0.0824` |
| V17 Epoch-1 (step 3,500) | [`vineetdaniels/NYXMed-V17-Epoch1`](https://huggingface.co/vineetdaniels/NYXMed-V17-Epoch1) | `eval_loss = 0.0875`, frozen for safety |
| V16 (base) | [`vineetdaniels/NYXMed-V16-Model`](https://huggingface.co/vineetdaniels/NYXMed-V16-Model) | Required to load this adapter |

Adapter weights (`adapter_model.safetensors`) are 1.66 GB. Full training history is available in `training_metrics.json` and TensorBoard logs in this repo under `logs/`.

---

## Acknowledgements

Built on Meta's [Llama-3](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) via Hugging Face's `transformers`, `peft`, `accelerate`, and `deepspeed` libraries.