Create README.md

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text-generation
+library_name: peft
+language:
+- en
+tags:
+- qwen
+- 7b
+- peft
+- lora
+- qlora
+- 4-bit
+- healthcare
+- triage
+- nhs
+- uk
+- safety
+metrics:
+- perplexity
+---
+
+# DocMap UK Triage (Qwen2.5-7B-Instruct, QLoRA)
+
+LoRA adapters (and merged model variant) for a UK NHS-style healthcare triage/navigation assistant. The model provides safe routing (999/111/GP/UTC/Pharmacy), concise guidance, citations (when provided), and a brief non‑diagnostic disclaimer.
+
+## Intended use
+- Preliminary UK triage guidance and healthcare navigation.
+- Not a diagnostic tool. Not a medical device. Use under human oversight.
+
+## Quick start (adapters)
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+BASE = "Qwen/Qwen2.5-7B-Instruct"
+ADAPTER = "<your-username>/docmap-uk-triage-lorav1-qwen7b"
+
+tok = AutoTokenizer.from_pretrained(BASE, use_fast=True)
+if tok.pad_token is None: tok.pad_token = tok.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(
+    BASE, device_map="auto", load_in_4bit=True, trust_remote_code=True
+)
+model = PeftModel.from_pretrained(model, ADAPTER)
+
+SYSTEM = "You are DocMap's UK NHS triage assistant. Provide safe routing and a brief non‑diagnostic disclaimer."
+msgs = [{"role":"system","content":SYSTEM},{"role":"user","content":"Headache 2 days, no vomiting."}]
+prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+with torch.inference_mode():
+    out = model.generate(**inputs, max_new_tokens=320, temperature=0.2, top_p=0.9, repetition_penalty=1.03)
+print(tok.decode(out[0], skip_special_tokens=True))
+```
+
+## Quick start (merged model)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+MODEL_ID = "<your-username>/docmap-uk-triage-merged-qwen2.5-7b"
+tok = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=True)
+m = AutoModelForCausalLM.from_pretrained(MODEL_ID, device_map="auto", torch_dtype=torch.bfloat16)
+
+msgs = [{"role":"system","content":"You are DocMap's UK NHS triage assistant."},
+        {"role":"user","content":"Chest discomfort on exertion, no sweating, no breathlessness."}]
+prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
+ids = tok(prompt, return_tensors="pt").to(m.device)
+with torch.inference_mode():
+    out = m.generate(**ids, max_new_tokens=320, temperature=0.2, top_p=0.9)
+print(tok.decode(out[0], skip_special_tokens=True))
+```
+
+## Training summary
+- Method: QLoRA (bitsandbytes 4‑bit nf4, double-quant, bfloat16 compute)
+- LoRA: r=32, alpha=32, dropout=0.05; targets: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj
+- Seq len: 4096; batch: 1; grad_accum: 16; epochs: 3; lr: 2e‑4; gradient checkpointing
+- Data: Supabase train bucket (`lorav1/**` JSONL: instruction, input?, output, citations)
+
+## Evaluation (hold‑out 10%)
+- Eval loss ≈ 0.243 → perplexity ≈ 1.27 (in‑distribution)
+
+## Safety
+- This is general information, not a medical diagnosis.
+- Emergencies: call 999. Urgent concerns: contact NHS 111.
+- Verify outputs clinically; model may miss edge cases.
+
+## Files
+- Adapters: `adapter_model.safetensors`, `adapter_config.json`
+- Merged: `model.safetensors` (shards), `config.json`, tokenizer files, `chat_template.jinja` (default DocMap system)
+
+## License
+- Adapters and merged distribution: Apache‑2.0.
+- Base model and any third‑party content follow their respective licenses.
+
+## Acknowledgements
+- Base: Qwen/Qwen2.5‑7B‑Instruct
+- Tooling: PEFT, Transformers, TRL, bitsandbytes
+- UK healthcare sources (NHS and reputable UK providers)