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covaga
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Model Details
Model Provider
covaga
Model Tree
Qwen/Qwen3.5-9B
Input Modalities
Output Modalities
Supported Functionality
python
# pip install "transformers>=5" unsloth peft pillowfrom unsloth import FastVisionModelfrom peft import PeftModelfrom PIL import Image model, processor = FastVisionModel.from_pretrained( "Qwen/Qwen3.5-9B", load_in_4bit=False, load_in_16bit=True, max_seq_length=2048)model = PeftModel.from_pretrained(model, "covaga/eplan-2027-lora-vision")FastVisionModel.for_inference(model) img = Image.open("symbol.png").convert("RGB")msgs = [{"role": "user", "content": [ {"type": "image", "image": img}, {"type": "text", "text": "Identify this EPLAN electrical symbol and describe its shape."}]}]inputs = processor.apply_chat_template( msgs, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True).to(model.device)out = model.generate(**inputs, max_new_tokens=300)print(processor.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))Requires
transformers>=5. Needs ≥24 GB VRAM for BF16 (or quantize / use the merged GGUF ecosystem below).
short_name + variant| Aspect | Result |
|---|---|
| Shape/structure description | ✅ Accurate — correct primitives, layout, connection points |
Symbol short_name recall | ⚠️ 1/24 — arbitrary ID memorization is hard; use visual RAG for naming (below) |
Example of what it produces — given a mushroom pushbutton symbol:
"A vertical line segment. A right-pointing triangle attached to the right side… a horizontal dashed line extending from the tip of the triangle."
covaga/electrical-symbols-dataset
— 33,502 EPLAN symbol images with short_name, catalog number, IEC/NFPA
description, variant and visual_description. Trained on 24k samples,
split by short_name so test symbols were never seen.
Exact short_name recall needs retrieval, not memorization: embed the query
image (CLIP/SigLIP), look up nearest neighbors in the symbol dataset, feed
the real names as candidates. Related tooling:
covagashi/eplan-rag-mcp — EPLAN MCP server, verified 2027 action registriescovagashi/eplan-cloudflare-rags — hosted RAG over official EPLAN 2027 docscovagashi/eplan-development-skill — curated EPLAN dev reference for agentscovaga/eplan-2027-lora-v3 — text adapter: EPLAN 2027 API Q&A + C# scripting (EN/ES)covaga/eplan-2027-9b-merged — merged BF16 text modelcovaga/eplan-2027-9b-merged-GGUF — Q4_K_M for local use| Setting | Value |
|---|---|
| Method | Vision LoRA (Unsloth FastVisionModel), BF16 |
| Layers tuned | vision encoder + language attention/MLP (102M params, 1.07%) |
| Samples | 24,000 image QA pairs, 1 epoch (1,500 steps, eff. batch 16) |
| Final loss | 0.50 avg · 0.24 last quarter |
| Hardware | A100 80GB, 2.3 h, 19.5 GiB VRAM peak |
- Weak on exact symbol IDs (1/24) — pair with image retrieval for production naming.
- Long answers can burn the token budget on shape analysis before naming; give it ≥300 tokens or ask for the name first.
- Isolated symbols only — it was not trained on full schematic pages.
- Dataset noise exists (some auto-generated descriptions are garbled).
- Not a replacement for EPLAN: verify against the official symbol library.
- Base model
Qwen/Qwen3.5-9Blicense terms apply.
bibtex
@misc{eplan-2027-lora-vision, author = {Lopez, Christian D.}, title = {EPLAN Electrical Symbol Recognition — Qwen3.5-9B Vision LoRA}, year = {2026}, url = {https://huggingface.co/covaga/eplan-2027-lora-vision}}Part of the covaga.xyz open EPLAN tooling — free Spanish EPLAN scripting guide, RAG endpoints, datasets and models.
# pip install "transformers>=5" unsloth peft pillowfrom unsloth import FastVisionModelfrom peft import PeftModelfrom PIL import Image model, processor = FastVisionModel.from_pretrained( "Qwen/Qwen3.5-9B", load_in_4bit=False, load_in_16bit=True, max_seq_length=2048)model = PeftModel.from_pretrained(model, "covaga/eplan-2027-lora-vision")FastVisionModel.for_inference(model) img = Image.open("symbol.png").convert("RGB")msgs = [{"role": "user", "content": [ {"type": "image", "image": img}, {"type": "text", "text": "Identify this EPLAN electrical symbol and describe its shape."}]}]inputs = processor.apply_chat_template( msgs, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True).to(model.device)out = model.generate(**inputs, max_new_tokens=300)print(processor.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))@misc{eplan-2027-lora-vision, author = {Lopez, Christian D.}, title = {EPLAN Electrical Symbol Recognition — Qwen3.5-9B Vision LoRA}, year = {2026}, url = {https://huggingface.co/covaga/eplan-2027-lora-vision}}