Instructions to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora")

llama-cpp-python

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora",
	filename="qwen-edgeai-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Use Docker

docker model run hf.co/eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

LM Studio
Jan

vLLM

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Ollama
How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with Ollama:
```
ollama run hf.co/eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M
```

Unsloth Studio new

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora to start chatting

Pi new

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with Docker Model Runner:
```
docker model run hf.co/eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M
```

Lemonade

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Run and chat with the model

lemonade run user.edgeai-docs-qwen2.5-coder-0.5b-lora-Q4_K_M

List all available models

lemonade list

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

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

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

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

@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}}
}

Downloads last month: 174

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

4-bit

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

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