Train & Fine-Tune Models
Medical Reasoning via LoRA Fine-Tuning
Gemma-3-12B adapted for symptom triage, drug interactions, and lab interpretation: 7.8GB GGUF for fully offline clinical use.
The problem
Clinical teams need domain-specific medical reasoning for symptom triage, drug interactions, and lab interpretation without sending patient data to an external API.
- A general-purpose model doesn't know your formulary or your intake protocol.
- Compliance won't sign off on any call that sends PHI to a third-party API.
- Every symptom-triage judgment call today means paging a clinician for something routine.
What NEO built
NEO fine-tuned Gemma-3-12B on medical reasoning tasks via LoRA adapters, then quantized the result to GGUF for fully offline deployment.
The result
7.8GB offline GGUF
Produced a 7.8GB offline GGUF checkpoint that runs clinical reasoning entirely on-premise with no internet dependency.
From the blog · 8 min
Gemma 3 12B Medical SFT: Offline Clinical Reasoning via LoRA Fine-Tuning
NEO built a supervised fine-tuning pipeline that adapts Gemma-3-12B for medical reasoning across symptom triage, drug interactions, and lab interpretation, exporting a 7.8 GB GGUF for fully offline clinical decision support.
Try this in your workspace
Paste this into NEO chat to kick off the same workflow on your own data.
Fine-tune Gemma-3-12B on my medical Q&A dataset using LoRA adapters for symptom triage and drug-interaction reasoning, then quantize the result to GGUF so it runs fully offline on-prem.
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