LLM Engineering & Inference

Clinical-Grade Serving

Objective: Sub-200ms TTFT on clinical inference workloads

Method: PagedAttention + continuous batching + GGUF 4-bit

Hardware: NVIDIA H100 | vLLM | Flash-Decoding

Naive model deployment leads to GPU memory fragmentation and unacceptable Time-To-First-Token (TTFT). We implement advanced serving architectures using PagedAttention and continuous batching on specialized medical models.

By leveraging custom CUDA kernels and optimizing precision states (GGUF 4-bit quantization), we maximize hardware utilization — achieving high token throughput on clinical inference workloads without sacrificing diagnostic reasoning quality.

inference-engine.log

> [INFO] Initializing vLLM Engine (GPU 0: NVIDIA H100)
> [INFO] Loading weights: google/MedGemma-4B-IT
> [INFO] Quantization: GGUF (4-bit) | Precision: bfloat16
> [INFO] Allocating KV Cache (PagedAttention)...
> [INFO] Max sequence length: 8192 | Block size: 16
> [CUDA] Flash-Decoding activated for generation phase.
> [METRIC] TTFT: 142ms | TPOT: 18ms | Throughput: 4,200 tok/s
> [STATUS] Engine ready. Listening on port 8000...

Medical Triage Agent Router

Objective: Mathematically guarantee valid clinical output

Method: Logits-level token masking via Outlines

Schema: Pydantic + SNOMED/ICD-10 deterministic routing

LLMs are probabilistic text generators, which makes them inherently dangerous for clinical automation. "Prompting" them to output structured JSON frequently fails at the edge — where patient safety matters most.

We deploy frameworks that intervene directly at the logits level during decoding. By masking invalid tokens before sampling, we mathematically guarantee that the model output conforms to clinical Pydantic schemas — including SNOMED/ICD-10 entity mapping and deterministic routing actions.

triage_router.py

import outlines
from pydantic import BaseModel, Field
from enum import Enum

# Clinical routing action space
class TriageAction(Enum):
    ROUTE_ONCOLOGY = "ROUTE_ONCOLOGY"
    ROUTE_CARDIOLOGY = "ROUTE_CARDIOLOGY"
    MAPPED_ICD10 = "MAPPED_ICD10"
    ESCALATE_HITL = "ESCALATE_HITL"

# Strict neuro-symbolic execution schema
class TriageExecutionPlan(BaseModel):
    action: TriageAction
    snomed_code: str = Field(pattern="^[0-9]{6,10}$")
    icd10_mapping: str = Field(max_length=10)
    confidence: float = Field(ge=0.0, le=1.0)
    reasoning_chain: list[str]

# Bind MedGemma and constrain logits deterministically
model = outlines.models.vllm("google/MedGemma-4B-IT")
generator = outlines.generate.json(model, TriageExecutionPlan)

# 100% Guaranteed valid clinical routing plan
result = generator(
    "Persistent cough, weight loss, elevated CEA. Route."
)

Medical Chain-of-Thought Without Catastrophic Forgetting

Objective: Domain-specific clinical reasoning without drift

Method: Unsloth QLoRA + DPO on Medical-CoT datasets

Output: LoRA adapters exported to safetensors

When off-the-shelf models fail at domain-specific clinical reasoning, we align them to your proprietary medical data. Using Unsloth for ultra-fast gradient computation and DPO (Direct Preference Optimization), we teach models exactly how to reason within clinical constraints.

Medical-Chain-Of-Thought datasets ensure structured diagnostic reasoning while minimizing hallucination — drastically reducing training costs on specialized medical workloads.

fine-tune-medical.log

> [TRAIN] Initiating Unsloth QLoRA fine-tuning sequence...
> [MEMORY] Base Model: MedGemma-4B-IT | 4-bit | VRAM: 5.2GB
> [OPTIMIZE] Triton kernels enabled for RoPE and CrossEntropyLoss.
> [DATA] Loading Medical-Chain-Of-Thought Dataset (N=15,400)
> [EPOCH 1/3] Step 50/480 | Loss: 1.402 | Grad Norm: 0.8
> [EPOCH 2/3] Step 100/480 | Loss: 0.984 | Grad Norm: 0.4
> [ALIGN] Applying Direct Preference Optimization (DPO)...
> [EVAL] Reward margin increasing. Hallucination rate dropping.
> [SAVE] Exporting LoRA adapters to safetensors... DONE.

Clinical-Grade Inference, Structured Generation, Neuro-Symbolic Governance

Clinical-Grade Serving

Medical Triage Agent Router

Medical Chain-of-Thought Without Catastrophic Forgetting

Stop paying for bloated API wrappers.

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