Instructions to use Pablo-Flores-Mollinedo/verilog-qwen2.5-coder-7b-v16-pilot-5k-from-v9-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Pablo-Flores-Mollinedo/verilog-qwen2.5-coder-7b-v16-pilot-5k-from-v9-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct") model = PeftModel.from_pretrained(base_model, "Pablo-Flores-Mollinedo/verilog-qwen2.5-coder-7b-v16-pilot-5k-from-v9-lora") - Notebooks
- Google Colab
- Kaggle
Verilog Qwen2.5-Coder 7B v16 Pilot 5K from v9 LoRA
Continuation from v9 with retention mix plus 5K verified synthetic_v2 pilot examples.
VerilogEval v2 direct result:
- Compile: 138/156 = 88.46%
- Functional pass: 60/156 = 38.46%
Caveat: benchmark-targeted research adapter. Not a clean zero-shot leaderboard claim.
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