Update model card

README.md

@@ -1,19 +1,110 @@
 ---
+license: other
+base_model: unsloth/Qwen2.5-3B-Instruct-bnb-4bit
 tags:
-- gguf
-- llama.cpp
-- unsloth
+  - clinical
+  - medical
+  - healthcare
+  - qlora
+  - unsloth
+  - chatml
+  - rapha
+language:
+  - en
 ---
 
-# rapha : GGUF
+# Rapha — Clinical AI Physician Assistant
 
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
+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.**
 
-**Example usage**:
-- For text only LLMs:    `llama-cli -hf Phora68/rapha --jinja`
-- For multimodal models: `llama-mtmd-cli -hf Phora68/rapha --jinja`
+- **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
 
-## Available Model files:
-- `merged.Q4_K_M.gguf`
-This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+## 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.*