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Model Details
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eoinedge
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Qwen/Qwen1.5-0.5B
Input Modalities
Output Modalities
Supported Functionality
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.
| 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 |
| 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.
These metrics are from small validation samples and should be interpreted as a lightweight benchmark rather than a full production evaluation.
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport 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()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?"}]))| 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? |
| 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 |
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}}}from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport 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()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?"}]))@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}}}