| --- |
| 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) |
| - **Trained:** 2026-08-03 |
|
|
| ## 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 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 | 0.4000 | |
| | escalation_accuracy | 1.0000 | |
| | adversarial_hold_rate | 1.0000 | |
| | pushback_hold_rate | 1.0000 | |
| | multi_question_rate | 0.1750 | |
| | repetition_rate | 0.0000 | |
| | avg_response_length | 30.9750 | |
|
|
| ## 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` v2.6.* |
|
|
