This adapter fine-tunes Qwen/Qwen2.5-Coder-0.5B-Instruct on a combined Arduino + Edge AI corpus to provide a single offline-capable model for both Arduino documentation guidance and Edge AI tooling knowledge.

Model Details

Model type: LoRA adapter
Base model: Qwen/Qwen2.5-Coder-0.5B-Instruct
Adapter size: 0.5B
Training corpora: subset/subset_arduino + subset/subset_edgeai
Task: text generation / coding assistance
Library: peft
Pipeline: text-generation

Intended Use

This adapter is intended for users who need offline or local inference with both Arduino documentation knowledge and Edge AI reference material in one model.

Training Details

Training strategy: LoRA fine-tuning on combined subset corpora
Hyperparameters: 3 epochs, batch size 2, gradient accumulation 4, learning rate 1e-4
Data processed: combined subset of Arduino docs and Mintlify Edge AI docs
Output directory: adapter-combined-subset-0.5b

Evaluation

QA Evaluation

Dataset: 10 fixed Arduino-domain questions
Base model avg response time: 6.41s
Adapter avg response time: 6.57s
Base keyword hits avg: 7.0
Adapter keyword hits avg: 5.5
Base code snippet presence: 9/10
Adapter code snippet presence: 1/10
Result file: eval_combined_0.5b_results.csv

Perplexity Evaluation

Test corpus: subset/subset_edgeai (121 docs)
Base mean PPL: 9.46
Adapter mean PPL: 6.65
Mean delta: -2.81
Adapter wins: 117 / 121 files
Result file: ppl_combined_0.5b_subset_edgeai_results.csv

Notes

Lower perplexity indicates the adapter is more confident on Edge AI domain text.
The QA evaluation is a small fixed question set, and the perplexity results are the deeper numeric signal for domain adaptation.

References

Offline SLMs for Edge AI Development — Part 1: Qwen LoRA Adapter Fine-Tuned on Edge Impulse Docs
Offline SLMs for Edge AI Development — Part 2: RAG as an Enhancement for Fine-Tuned Models with FAISS
Offline SLMs for Edge AI Development — Part 3: Agentic Coding with an Arduino Fine-Tuned Adapter via llama.cpp and OpenCode

How to use

python

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from peft import PeftModel

base = "Qwen/Qwen2.5-Coder-0.5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base)
model = PeftModel.from_pretrained(model, "eoinedge/arduino-edgeai-qwen-combined-0.5b-lora")
model = model.merge_and_unload()
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device="cpu")
print(pipe("How do I read a DHT11 sensor in Arduino?", max_new_tokens=200)[0]["generated_text"])

License

This adapter is derived from the base model and follows the licensing terms of the base model and related adapter resources.