license, license_name, license_link, base_model, language, pipeline_tag, tags

license	license_name	license_link	base_model	language	pipeline_tag	tags
other	ohmatic-sal-1.1	LICENSE	Qwen/Qwen3-8B	en	text-generation	circuit-design schematic-generation electronics erc qwen3 self-correction gguf

Ohmatic-Qwen3-8B

Ohmatic generates electronic circuit schematics from natural-language descriptions and corrects its own designs against an Electrical Rule Checker (ERC). It is a fully assembled (merged, no adapter required) 8B model based on Qwen3-8B.

How it works

Ohmatic is trained to operate as a closed verification loop, not a one-shot generator:

1. Forward generation - the user describes a circuit in plain language; the model emits a complete structured schematic (components, values, nets).
2. ERC verification - the schematic is checked by a deterministic Electrical Rule Checker (shorts, floating nets, missing references, polarity/supply errors, unclosed structures).
3. Self-correction - on ERC failure, the model receives the rule-checker findings and emits a repaired schematic. Training explicitly teaches this correction turn, so the model improves designs rather than re-rolling them.

Training

Trained to both produce circuits and repair its own designs from ERC feedback, using only ERC-verified examples. The released weights are fully merged - load like any causal LM, no PEFT/adapter required.

• Base: Qwen3-8B (bf16)
• The training data, recipe, and ERC engine are proprietary; this card documents the model artifact you run.

Files

File	Format	Use
*.safetensors	bf16, sharded	transformers / vLLM serving, further finetuning
Ohmatic-Qwen3-8B-Q8_0.gguf	GGUF 8-bit	llama.cpp / LM Studio / ollama - near-lossless
Ohmatic-Qwen3-8B-Q4_K_M.gguf	GGUF 4-bit	llama.cpp on consumer hardware

Usage (transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
m  = AutoModelForCausalLM.from_pretrained("VittoriaLanzo/Ohmatic-Qwen3-8B",
                                          torch_dtype="bfloat16", device_map="auto")
tk = AutoTokenizer.from_pretrained("VittoriaLanzo/Ohmatic-Qwen3-8B")
msgs = [{"role": "user", "content": "Design a 5V-to-3.3V LDO supply with input protection."}]
x = tk.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(m.device)
print(tk.decode(m.generate(x, max_new_tokens=2048)[0], skip_special_tokens=True))

Usage (llama.cpp)

llama-cli -m Ohmatic-Qwen3-8B-Q4_K_M.gguf -cnv \
  -p "Design an astable 555 timer blinking an LED at 1 Hz on 9V."

Evaluation

Held-out ERC pass rate at selection time (n=32 in-training eval): 53.1% first-pass validity, with the correction loop recovering a further share of failures. This is the single-shot held-out number; the full product-pipeline benchmark (normalization + correction loop + killswitch, judged by the same ERC engine) is reported in the Ohmatic repository.

License

Ohmatic Source-Available License 1.1 (Ohmatic-SAL-1.1) - adapted from the Functional Source License 1.1, but it is not the FSL: the only change is a 10-year change date (instead of two), after which the grant converts to Apache-2.0. Full text in LICENSE. Source-available, not open source: any Permitted Purpose is allowed, a Competing Use is not. (Base model Qwen/Qwen3-8B is separately licensed; these merged weights are Ohmatic-SAL-1.1.)