eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora

edgeai-docs-embedding-qwen1.5-0.5b-instruct

A lightweight LoRA adapter fine-tuned on 1,794 Edge Impulse / Edge AI MDX documentation files from the Edge Impulse documentation, built on top of `Qwen/Qwen1.5-0.5B`.

Optimized for:

• —answering developer questions about Edge Impulse Studio, SDKs, APIs, and tooling
• —summarizing technical documentation and tutorials
• —generating code snippets for edge ML workflows
• —lightweight local/edge deployment with PEFT adapters

Larger variants in training: 1.5B · 7B (Qwen2.5-Coder base)

Model Summary

edgeai-docs-embedding-qwen1.5-0.5b-instruct is a PEFT LoRA adapter trained for documentation-focused text generation and conversational support over Edge Impulse / Edge AI knowledge.

Use cases

• —Documentation Q&A for Edge Impulse developers
• —Technical explanation of Studio workflows, SDK usage, and hardware deployment
• —Generating sample code for API, CLI, and Python SDK integrations
• —Retrieval-augmented generation (RAG) over Edge AI docs

Model Details

Training Data

Topics covered: Studio projects, datasets, data ingestion, DSP and transformation blocks, learning and processing blocks, model deployment, Python SDK, REST API, CLI tools, and edge inference.

Evaluation

QA evaluation

• —Dataset: 5 fixed developer-style prompts
• —Base avg keyword count: 8.2
• —Adapter avg keyword count: 6.8
• —Code snippet presence: 5/5 for both base and adapter

Perplexity on Edge AI samples

• —Test corpus: 30 sample Edge AI documentation files
• —Base mean perplexity: 11.53
• —Adapter mean perplexity: 12.02
• —Adapter wins: 4 / 30 documents

These metrics are from small validation samples and should be interpreted as a lightweight benchmark rather than a full production evaluation.

Tutorials

• —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

Usage

Load with PEFT

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

BASE_MODEL = "Qwen/Qwen1.5-0.5B"
ADAPTER = "eoinedge/edgeai-docs-embedding-qwen1.5-0.5b-instruct"

device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    torch_dtype=torch.float16 if device != "cpu" else torch.float32,
    device_map=device,
)
model = PeftModel.from_pretrained(base_model, ADAPTER)
model.eval()

Text generation pipeline

from transformers import pipeline

pipe = pipeline("text-generation", model="eoinedge/edgeai-docs-embedding-qwen1.5-0.5b-instruct")
print(pipe([{"role": "user", "content": "How do I use the Edge Impulse Python SDK to upload data?"}]))

Example prompts

Limitations

• —Based on a 0.5B base model — may struggle with long multi-step reasoning
• —Training data covers Edge Impulse docs as of mid-2026; newer features may be missing
• —May hallucinate or fabricate undocumented APIs or block behavior
• —Not validated for safety-critical or production use
• —Validate generated code before deploying on hardware

Related models

Citation

@misc{edgeai-docs-embedding-qwen1.5-0.5b-instruct,
  author       = {Jordan, Eoin},
  title        = {edgeai-docs-embedding-qwen1.5-0.5b-instruct},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/eoinedge/edgeai-docs-embedding-qwen1.5-0.5b-instruct}}
}