license: other
base_model: unsloth/Qwen2.5-3B-Instruct-bnb-4bit
tags:
- clinical
- medical
- healthcare
- qlora
- unsloth
- chatml
- rapha
language:
- en
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: 2,048 tokens (training)
- Trained: 2026-08-06
Training architecture (v2.6)
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 + Stage2 | Complaint identification + OPQRST symptom detail |
| 2 | Stage3 + Stage4 | Medical history + red-flag triage |
| 3 | FullArc + Adversarial | Complete session flows + safety robustness |
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 6 datasets: Stage 1 greetings, Stage 2 OPQRST symptom exploration, Stage 3 medical history, Stage 4 red-flag screening, a multi-turn adversarial set (self-diagnosis, symptom denial, medication refusal, minimised red flags, prompt injection), and full-arc sessions ending in a Stage 5 handoff. Followed by DPO preference alignment on a de-duplicated, leak-safe train/val split (held out by source scenario).
Records actually used in this run (after quality filtering, de-duplication and per-stage caps): 24,000 total.
| Stage | Records |
|---|---|
| Adversarial | 3,000 |
| FullArc | 4,500 |
| Stage1 | 4,500 |
| Stage2 | 4,500 |
| Stage3 | 3,500 |
| Stage4 | 4,000 |
Clinical content provenance
The interview question policy, red-flag screening, and the Suspicions for
clinician consideration section in each full-arc report are generated by a
deterministic forward-chaining rule engine (clinical_rules.py), not by the
source CSV — whose doctor_response column contains only 6 distinct strings
across 50,000 rows and is unused. Those rules are author-specified from
published triage criteria and have not been clinician-validated. Suspicions
are hypotheses for a clinician to investigate, never conclusions told to the
patient; Rapha still never diagnoses.
Eval metrics (last training run)
| Metric | Value |
|---|---|
| empathy_rate | 0.3000 |
| escalation_accuracy | 0.5000 |
| adversarial_hold_rate | 0.5000 |
| pushback_hold_rate | 0.5000 |
| multi_question_rate | 0.4500 |
| repetition_rate | 0.0000 |
| suspicion_section_rate | 1.0000 |
| avg_response_length | 42.0500 |
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=2048,
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.
Generated automatically by train_rapha_llm.py v2.6.