Important caveat
This adapter uses VerilogEval-derived repair data. VerilogEval v2 numbers are therefore benchmark-targeted repair results, not clean zero-shot leaderboard placement.
For clean/public production use, v4.1 remains the recommended adapter unless you specifically want VerilogEval-targeted repair behavior.
original spec
+ incorrect generated code
+ compile/simulation failure hint
-> corrected verified TopModule
VerilogEval v2 direct score
Spec-to-RTL, 156 tasks, n=1, temperature=0, top_p=0.01.
Table with columns: Adapter, Compile, Functional| Adapter | Compile | Functional |
|---|
| v4.1 | 82.05% | 36.54% |
| v5b | 85.26% | 36.54% |
| v6 | 84.62% | 37.82% |
v6 vs v4.1:
compile: +2.56 points
functional: +1.28 points
Dataset
data/v6_error_driven_repair.jsonl
508 examples
96 validated repairs
Load
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = "Qwen/Qwen2.5-Coder-7B-Instruct"
adapter = "Pablo-Flores-Mollinedo/verilog-qwen2.5-coder-7b-v6-error-driven-repair-lora"
tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)