---
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)

```bash
ollama create rapha -f Modelfile.q4_k_m
ollama run rapha
```

## Usage — Transformers (LoRA adapter)

```python
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.*