---
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:** 8,192 tokens (training) / 4,096 (Ollama default)
- **LoRA config:** r=32, alpha=32, target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- **Trained:** 2026-07-22

## Training architecture (v2.3)

Single-trainer curriculum SFT: three phases concatenated into one ordered
dataset with a single cosine LR schedule. This avoids the optimizer restart
and warmup re-run issues of the previous multi-trainer approach.

| 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 |
| `datasets/` | Raw JSONL training/validation/DPO datasets used for this run |

## Training data

**Total training records: ~159,766**

| Dataset | Records | File |
|---|---|---|
| Stage 1 — Greetings | 25,000 | `stage1_greetings.jsonl` |
| Stage 2 — OPQRST | 40,344 | `stage2_opqrst.jsonl` |
| Stage 3 — History | 40,000 | `stage3_history.jsonl` |
| Stage 4 — Red Flags | 14,422 | `stage4_red_flags.jsonl` |
| Adversarial | 10,000 | `adversarial.jsonl` |
| Full-Arc Conversations | 30,000 | `full_arc_conversations.jsonl` |
| Validation | 1,100 | `val_sharegpt.jsonl` |
| DPO Preference Pairs | 3,000 | `preference_pairs.jsonl` |

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 — a portion include a patient pushback turn to test
boundary-holding). Followed by DPO preference alignment on the pairs above.

### Red-flag terms screened for

`worst headache`, `can't feel my legs`, `arm feels heavy and chest`, `can't breathe`, `loss of consciousness`, `sudden vision`, `neck stiff`, `light hurts`, `confused`, `won't stop bleeding`, `bilateral leg weakness`, `bladder dysfunction`, `tachypnoea`, `thunderclap`, `cauda equina`, `qsofa`, `meningitis`

## Eval metrics (last training run)

| Metric | Value |
|---|---|
| _(no eval metrics captured)_ | — |

## 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=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.

---
*Generated automatically by `train_rapha_llm.py` v2.3.*