---
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- verilog
- rtl
- qwen2.5-coder
- lora
- peft
---

# verilog-qwen2.5-coder-7b-v9-auto-distilled-direct-lora

LoRA/PEFT adapter for Verilog RTL code generation, trained from `Qwen/Qwen2.5-Coder-7B-Instruct` through the project adapter chain.

## Intended use

Load base model `Qwen/Qwen2.5-Coder-7B-Instruct` and this adapter with PEFT. Prompt for complete synthesizable Verilog. This repo contains adapter files plus tokenizer/config dependencies needed for PEFT loading.

## Local VerilogEval v2 spec-to-RTL direct result

- Compile: 135/156 = 86.54%
- Functional pass: 67/156 = 42.95%

Best direct first-pass adapter in this experiment series.

## Caveat

This adapter is benchmark-targeted. Training data includes VerilogEval-distilled passing outputs and verified/retention data. Do not present these numbers as clean zero-shot leaderboard results.

## Training summary

- Base adapter input: v8 combined verified direct (for v9); v9 auto distilled direct (for v10)
- Dataset style: direct `spec -> verified passing Verilog code`
- LoRA r/alpha: 16/32
- LR: 3e-7
- Epochs: 1
- Checkpointing: enabled every 25 steps during training

## Loading sketch

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base = "Qwen/Qwen2.5-Coder-7B-Instruct"
adapter = "Pablo-Flores-Mollinedo/verilog-qwen2.5-coder-7b-v9-auto-distilled-direct-lora"

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