---
license: cc-by-nc-4.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: peft
tags: [verilog, rtl, eda, lora, question-answering]
---

# CircuitQA LoRA adapters

LoRA adapters (r = 64, alpha = 128; weights stored in fp32, base model loaded in bf16) for
[Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct), trained by
the authors of *CircuitQA: A Grounded Architecture for Question Answering over Digital RTL Designs
with Multi-Backend Query Routing*. Data, code and evaluation outputs:
[drawais/circuitqa](https://huggingface.co/datasets/drawais/circuitqa). Only the adapter weights and
configurations are provided; the base model and its tokenizer are loaded from their own repository.

| Folder | Role |
|---|---|
| `s3_seed42`, `s3_seed123`, `s3_seed456` | Response Decoder trained on the template questions of the repository-disjoint split (three seeds) |
| `router_s3` | Query Routing Module trained on the same template questions |
| `s2_seed42`, `s2_seed123`, `s2_seed456` | Response Decoder trained on the template questions of the design-disjoint split (three seeds) |

All adapters are trained on template questions and answers computed from the outputs of the analysis
backends, from an earlier build of the template corpus of
[drawais/circuitqa](https://huggingface.co/datasets/drawais/circuitqa) (the released `corpus/`
recomputes every answer with the released backends).

Requires `peft>=0.21` (the configs were written by PEFT 0.21.2).

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = "Qwen/Qwen2.5-Coder-7B-Instruct"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(model, "drawais/circuitqa-adapters", subfolder="s3_seed42")
```

## Licence

The adapter weights and configurations in this repository are licensed under
[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (non-commercial use). They are LoRA
weights trained by the authors on top of Qwen2.5-Coder-7B-Instruct; the base model is not included and
remains under its own licence, the Apache License 2.0 (copy in `LICENSE-Qwen2.5-Coder-7B-Instruct`).
Use of the base model is subject to that licence.

**Training data.** The training data refer to designs from the upstream repositories listed in the
dataset's `LICENSE.md`; the adapters contain no design source code. Training data derived from NVDLA
designs contain data derived from NVDLA, Copyright (c) 2016-2017 NVIDIA Corporation, licensed under
the NVIDIA Open NVDLA License and Agreement v1.0 (copy in `LICENSES/`), modified to contain only
analysis results computed by the authors. Training data derived from files of
[pConst/basic_verilog](https://github.com/pConst/basic_verilog) written by Konstantin Pavlov refer
to designs licensed under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), reduced to
analysis results computed by the authors. Upstream projects are named only to identify the sources
of the designs, without implying endorsement.

## Citation

If you use the CircuitQA dataset, code or adapters, please cite:

```bibtex
@misc{awais2026circuitqa,
  title        = {{CircuitQA}: A Grounded Architecture for Question Answering over Digital {RTL} Designs with Multi-Backend Query Routing},
  author       = {Awais, Muhammad and Postolache, Octavian Adrian and Oliveira, Sancho Moura},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/drawais/circuitqa}},
  note         = {Dataset and code}
}

@misc{awais2026circuitqaadapters,
  title        = {{CircuitQA} Adapters: {LoRA} Adapters for {Qwen2.5-Coder-7B-Instruct}},
  author       = {Awais, Muhammad and Postolache, Octavian Adrian and Oliveira, Sancho Moura},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/drawais/circuitqa-adapters}},
  note         = {Trained model adapters}
}
```