Important data note

This adapter was trained with manual repair targets only for the targeted v33 failure repairs. No converted VerilogEval RefModule reference-solution code was used as repair target code.

Intended format

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Thinking:
[BEGIN]
module TopModule(...);
  ...
endmodule
[DONE]

Use code inside [BEGIN] / [DONE] as final artifact.

Evaluation in this project

Local VerilogEval v2 spec-to-RTL direct single-adapter eval, no retry, no selector, no compiler feedback before final answer:

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v34 manual max2048: compile 103/156, pass 77/156 = 49.4%

A max4096 eval is being run after publication request to test whether max token budget closes the gap with v33 max4096.

Interpretation: v34 manual is experimental. v33 remains the best confirmed clean single-adapter checkpoint until the max4096 v34 result is known.

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python
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from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "Qwen/Qwen3.5-9B"
adapter = "Pablo-Flores-Mollinedo/verilog-qwen3.5-9b-v34-manual-structured-repair-lora"

tok = AutoTokenizer.from_pretrained(adapter, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(base, trust_remote_code=True, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

Notes

• Adapter only; requires the base model license/weights.
• Generated RTL should be compiled and simulated before use.
• Benchmark scores are finite-testbench simulation results, not formal proof.