Model description
This model fine-tunes Qwen2.5-0.5B-Instruct
using LoRA adapters to generate personalized Heart Rate Variability (HRV) feedback.
It uses lightweight parameter-efficient fine-tuning (PEFT), meaning only a small set of LoRA weights are trained while the base model remains frozen — making it fast and memory-efficient.
At runtime:
The base model (Qwen2.5-0.5B-Instruct) is loaded with transformers.
The LoRA adapter weights are merged dynamically using PeftModel.from_pretrained().
The model reads structured HRV data (e.g., RMSSD, StressScore, device type).
It interprets physiological patterns and generates human-style health insights in natural language.
The adapter specializes the base model in interpreting HRV metrics and generating concise wellness summaries that reflect stress balance, recovery capacity, and overall autonomic performance.
CODE :
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig, get_peft_model
from trl import SFTTrainer
from transformers import TrainingArguments
import os
import torch
BASE_MODEL = "Qwen/Qwen2.5-0.5B-Instruct" # alt: "Qwen/Qwen2.5-0.5B-Instruct"
data_path = "/kaggle/input/hrv-data/output.jsonl"
assert os.path.exists(data_path)
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, use_fast=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
model
model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
device_map="auto",
torch_dtype=torch.float16, # safe on all Kaggle GPUs
)
grad checkpointing can interfere; turn off for now
model.config.use_cache = False
LoRA
peft_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"],
task_type="CAUSAL_LM",
bias="none"
)
model = get_peft_model(model, peft_config)
force LoRA params to be trainable
for n, p in model.named_parameters():
if "lora" in n:
p.requires_grad = True
simple sanity check
trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
total = sum(p.numel() for p in model.parameters())
print(f"Trainable params: {trainable:,} / {total:,}")
==== data ====
dataset = load_dataset("json", data_files=data_path, split="train")
messages -> chat prompt
def build_prompt_from_messages(msgs):
sys_msg = next((m["content"] for m in msgs if m.get("role") == "system"), "")
usr_msg = next((m["content"] for m in msgs if m.get("role") == "user"), "")
return (
f"<|im_start|>system\n{sys_msg.strip()}<|im_end|>\n"
f"<|im_start|>user\n{usr_msg.strip()}<|im_end|>\n"
f"<|im_start|>assistant\n"
)
def formatting_func(samples):
texts = []
for msgs in samples["messages"]:
sys_msg = next((m["content"] for m in msgs if m.get("role") == "system"), "")
usr_msg = next((m["content"] for m in msgs if m.get("role") == "user"), "")
ans_msg = next((m["content"] for m in msgs if m.get("role") == "assistant"), "")
text = (
f"<|im_start|>system\n{sys_msg.strip()}<|im_end|>\n"
f"<|im_start|>user\n{usr_msg.strip()}<|im_end|>\n"
f"<|im_start|>assistant\n{ans_msg.strip()}{tokenizer.eos_token}"
)
texts.append(text)
return texts
==== training ====
train_args = TrainingArguments(
output_dir="/kaggle/working/qwen-hrv-lora",
num_train_epochs=2,
per_device_train_batch_size=1,
gradient_accumulation_steps=16,
learning_rate=2e-4,
lr_scheduler_type="cosine",
warmup_ratio=0.05,
logging_steps=10,
save_steps=200,
save_total_limit=2,
fp16=True, # use fp16 instead of bf16
optim="adamw_torch", # simple and stable without bnb
gradient_checkpointing=False,# off to avoid grad issues
max_grad_norm=0.3,
report_to="none"
)
trainer = SFTTrainer(
model=model,
tokenizer=tokenizer,
train_dataset=dataset,
formatting_func=formatting_func,
max_seq_length=1024, # trim if OOM
packing=False, # safer for small sets
args=train_args,
)
trainer.train()
==== save ====
adapter_dir = "/kaggle/working/qwen-hrv-lora/adapter"
trainer.model.save_pretrained(adapter_dir)
tokenizer.save_pretrained("/kaggle/working/qwen-hrv-lora")
print(f"Saved LoRA adapter to: {adapter_dir}")
INFERENCE :
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch, os
BASE_MODEL = "Qwen/Qwen2.5-0.5B-Instruct"
ADAPTER_PATH = "/kaggle/input/qwen-hrv/qwen-hrv-lora/adapter"
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, use_fast=True)
base = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
device_map="auto",
torch_dtype=torch.float16
)
ensure path is local, not a repo id
assert os.path.exists(os.path.join(ADAPTER_PATH, "adapter_config.json")), "adapter_config.json not found"
model = PeftModel.from_pretrained(base, ADAPTER_PATH, is_trainable=False)
model.eval()
sample HRV input
system_text = "You are a health insights assistant. Analyze HRV data."
user_text = "- baseline: 0.0\n- RMSSD: 110.01\n- StressScore: 53.28\n- device: other\n- RMSSD_zone: high\n- StressScore_zone: moderate"
prompt = f"<|im_start|>system\n{system_text}<|im_end|>\n<|im_start|>user\n{user_text}<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(
**inputs,
max_new_tokens=400,
temperature=0.3,
top_p=0.9,
pad_token_id=tokenizer.eos_token_id
)
result = tokenizer.decode(output[0], skip_special_tokens=True)
print(result.split("<|im_start|>assistant")[-1].strip())
RESULT :
system
You are a health insights assistant. Analyze HRV data.
user
- baseline: 0.0
- RMSSD: 110.01
- StressScore: 53.28
- device: other
- RMSSD_zone: high
- StressScore_zone: moderate
assistant
Your HRV (heart rate variability) profile shows some promising signs, but it also points to a mix of stress and recovery needs. Here’s an analysis based on the provided data:
Baseline: At this point, your heart rhythm is stable, indicating that long-term trends have been well managed.
RMSSD: High values reflect strong parasympathetic activity, which supports effective recovery and adaptability.
StressScore: Moderate levels suggest your system is handling some stress load effectively, though not entirely balanced.
device: Mentioning "other" indicates that while HRV readings come from multiple sources, they might not be directly linked to your personal lifestyle habits or environmental conditions.
Zone breakdown:
- High: This suggests your autonomic balance is currently favoring relaxation and recovery.
- Moderate: While not perfect, this reflects a healthy state with manageable stress load.
Overall assessment:
- Recovery potential is strong, as evidenced by the high RMSSD and moderate stress score.
- Stress regulation appears balanced, though the mixed zone signals that your body may still be under some strain.
Suggestions:
- Maintain current positive habits such as regular exercise, good sleep, and hydration to sustain recovery capacity.
- Incorporate relaxation practices like mindfulness, deep breathing, or meditation to reinforce resilience.
- Monitor future HRV readings closely to ensure they remain in line with your overall recovery patterns.
- Be aware that stress can sometimes fluctuate between high and moderate levels, so adjust routines accordingly if needed.
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