Rapha — Clinical AI Physician Assistant

Rapha conducts structured, empathetic clinical interviews across five stages (greeting, OPQRST symptom exploration, medical history, red-flag screening, escalation report) and hands a structured report to a physician. Rapha never diagnoses.

• Base model: unsloth/Qwen2.5-3B-Instruct-bnb-4bit
• Method: QLoRA (Unsloth) → curriculum SFT → DPO
• Chat template: ChatML
• Context window: 8,192 tokens (training) / 4,096 (Ollama default)
• Trained: 2026-07-30

Training architecture (v2.5)

Single-trainer curriculum SFT: three phases concatenated into one ordered dataset with a single cosine LR schedule. DPO uses a de-duplicated preference set with a held-out validation split (by unique prompt) and a corrected stage-aware system prompt (v2.3 had a bug where every DPO example was trained under the Adversarial system prompt, regardless of its actual stage — fixed in v2.4).

Phase	Data	Purpose
1	Stage1 + Stage4 + Adversarial	Interview boundaries + robustness
2	Stage2 + Stage3	Clinical content (OPQRST + history)
3	Full mix + FullArc	Complete session flows (Stage1→5)

Repo contents

Path	Contents
/ (root)	LoRA adapter (PEFT) — small, load on top of the base model
merged/	Full merged fp16 weights — standalone, no base model needed
gguf/	Quantised GGUF files (Q4_K_M, Q5_K_M, Q8_0) for Ollama / llama.cpp / LM Studio

Training data

Curriculum SFT across 5 datasets (~170k records): Stage 1 greetings, Stage 2 OPQRST symptom exploration, Stage 3 medical history, Stage 4 red-flag screening, and a multi-turn adversarial set (self-diagnosis, symptom denial, medication refusal, minimised red flags, prompt injection — ~50% with a patient pushback turn). Followed by DPO preference alignment on a de-duplicated, leak-safe train/val split.

Eval metrics (last training run)

Metric	Value
empathy_rate	0.2500
escalation_accuracy	0.0000
adversarial_hold_rate	1.0000
pushback_hold_rate	1.0000
multi_question_rate	0.0250
repetition_rate	0.0000
avg_response_length	36.2000

Usage — Ollama (GGUF)

ollama create rapha -f Modelfile.q4_k_m
ollama run rapha

Usage — Transformers (LoRA adapter)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="Phora68/rapha",
    max_seq_length=8192,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

Safety

Rapha is an information-gathering and triage-support tool. It is not a diagnostic device and must not be deployed without physician oversight. Red-flag detection and escalation responses should be validated against the clinical accuracy benchmark before any clinical use.

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Generated automatically by train_rapha_llm.py v2.5.