Task	Result
T1-bearing-protection	pass
T2-irrigation-interlock	pass
T3-async-sensor-poll	fail
T4-bearing-anomaly	pass
T5-hvac-setback	pass
T6-vitals-alert	pass
T7-co2-anomaly	pass

Failing: T3-async-sensor-poll

Model Details

Property	Value
Base model	`Qwen/Qwen2.5-Coder-1.5B-Instruct`
Adapter	LoRA QLoRA 4-bit
LoRA rank	r=16, alpha=32
Epochs	3
Train examples	5,479
Hardware	AMD RX 7900 GRE (ROCm, unsloth)

Usage

python

from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

model = AutoPeftModelForCausalLM.from_pretrained(
    "delimitter/qwen25-coder-1.5b-synoema-iot", device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("delimitter/qwen25-coder-1.5b-synoema-iot")

prompt = "Generate Synoema IoT rule: alert when temperature > 85C"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(out[0], skip_special_tokens=True))

delimitter/qwen25-coder-1.5b-synoema-iot

1.5B Synoema IoT Fine-tune

Eval Results

Model Details

Usage

Links

delimitter/
qwen25-coder-1.5b-synoema-iot