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

Property	Value
Base model	`Qwen/Qwen1.5-0.5B`
Adapter type	LoRA (PEFT)
LoRA rank (`r`)	8
LoRA alpha	32
Target modules	`q_proj`, `v_proj`
Task type	CAUSAL_LM
Trainable parameters	~786K (0.17% of base)
Training epochs	3
Batch size	4 (× grad accum 2 = effective 8)
Learning rate	3e-4
Max sequence length	512 tokens
Training hardware	Apple M1 Pro (MPS, fp16)
Precision	float16

Training Data

Stat	Value
Source	Edge Impulse documentation
File format	MDX (Markdown + JSX components)
Total files	1,794 `.mdx` files
Preprocessing	Stripped frontmatter, imports, JSX tags; unwrapped code fences; flattened links
Chunk size	512 tokens

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

Usage

Load with PEFT

python

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

python

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

Task	Prompt
Concept explanation	`What is a DSP block in Edge Impulse?`
API usage	`How do I use the Edge Impulse Python SDK to upload data?`
Deployment	`How do I deploy a model to an Arduino Nano 33 BLE Sense?`
Code generation	`Write Python code to collect IMU data and upload it to Edge Impulse.`
Troubleshooting	`Why is my Edge Impulse model showing high latency?`

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

Model	Base	Status
This model	Qwen/Qwen1.5-0.5B	✅ Available
eoinedge/edgeai-qwen2.5coder-1.5b-lora	Qwen2.5-Coder-1.5B-Instruct	🔄 Training
eoinedge/edgeai-qwen2.5coder-7b-lora	Qwen2.5-Coder-7B-Instruct	🔄 Training
eoinedge/arduino-qwen0.5-lora	Qwen/Qwen1.5-0.5B	✅ Available

Citation

bibtex

@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}}
}

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

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