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Ohmatic Source-Available License, Version 1.1
Abbreviation
Ohmatic-SAL-1.1
About this license
This license is adapted from the Functional Source License, Version 1.1
(FSL-1.1-ALv2). It is a modified version and is not the Functional Source
License. It differs from FSL-1.1-ALv2 in the following ways:
1. The Grant of Future License takes effect ten years after a version is made
available, instead of two, and is calculated separately for each version.
2. A Permitted Purpose additionally requires that your use fall within the
Community Tier defined below. The Community Tier thresholds are adapted from
the PolyForm Small Business License 1.0.0, with the headcount threshold set
to fewer than 50 individuals and an additional external-funding ceiling.
3. A Derivative Models restriction is added, adapted from the prohibition in the
Llama 2 Community License Agreement (section 1.b.v), prohibiting use of the
Software, its weights, or its Output to train any other model except with our
prior written permission.
4. Standard Acceptance, Term and Termination, and General provisions
(including governing law) are added for completeness.
Notice
Copyright 2026 Vittoria Lanzo
Terms and Conditions
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these Terms and Conditions. If you exercise those rights on behalf of an
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"Output" means any content, results, predictions, or other material generated by
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By using, copying, modifying, creating derivative works of, or redistributing
the Software, you accept and agree to these Terms and Conditions. If you do not
agree to these Terms and Conditions, you have no rights under them and may not
use the Software.
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Subject to your compliance with this License Grant and the Patents,
Redistribution, Derivative Models and Trademark clauses below, we hereby grant
you the right to use, copy, modify, create derivative works, publicly perform,
publicly display and redistribute the Software for any Permitted Purpose
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A Permitted Purpose is any purpose that is both (a) not a Competing Use and
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If your use exceeds the Community Tier, you may obtain a commercial license as
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---
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.)

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- endif %}
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{%- for forward_message in messages %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- set message = messages[index] %}
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{%- set ns.multi_step_tool = false %}
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{%- endif %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
{%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
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{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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"unsloth_fixed": true,
"use_cache": true,
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"vocab_size": 151936
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"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

1
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