---
license: other
license_name: ohmatic-sal-1.1
license_link: LICENSE
base_model: Qwen/Qwen3-8B
language:
- en
pipeline_tag: text-generation
tags:
- 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)

```python
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)

```bash
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](https://github.com/VittoriaLanzo/Ohmatic#benchmark).

## 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](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.)