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)

rabbitfishai/docmap-uk-triage-merged-qwen2.5-7b

DocMap UK Triage (Qwen2.5-7B-Instruct, QLoRA)