eoinedge

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

README

License: apache-2.0

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

Table with columns: Property, Value
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

Table with columns: Stat, Value
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

Table with columns: Task, Prompt
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

Table with columns: Model, Base, Status
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

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}}
}

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Model Details

Model Provider

eoinedge

Model Tree

Base

Qwen/Qwen1.5-0.5B

Adapter

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

Dedicated Endpoints

Container

Explore FriendliAI today

Get started Talk to an engineer

README

License: apache-2.0

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

Table with columns: Property, Value
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

Table with columns: Stat, Value
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

Table with columns: Task, Prompt
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

Table with columns: Model, Base, Status
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

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}}
}

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

README

Model Summary

Use cases

Model Details

Training Data

Evaluation

QA evaluation

Perplexity on Edge AI samples

Tutorials

Usage

Load with PEFT

Text generation pipeline

Example prompts

Limitations

Related models

Citation

Explore FriendliAI today

README

Model Summary

Use cases

Model Details

Training Data

Evaluation

QA evaluation

Perplexity on Edge AI samples

Tutorials

Usage

Load with PEFT

Text generation pipeline

Example prompts

Limitations

Related models

Citation