---
license: apache-2.0
base_model: google/gemma-2-12b-it
tags:
- verilog
- code-generation
- instruction-tuned
- vericoder
---

# Gemma-3-12B-IT (VeriCoder Dataset Ablation)

This is a fine-tuned version of Gemma-3-12B-IT model trained on VeriCoder dataset.

## Model Details

- **Base Model**: Gemma-3-12B-IT
- **Training Dataset**: VeriCoder dataset (126k samples)
- **Model Architecture**: Gemma3ForCausalLM
- **Parameters**: ~11.7B
- **Context Length**: 131,072 tokens
- **Sliding Window**: 1024

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "LLM4Code/VeriCoder_Gemma12b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0]))
```

## Training Details

- **Dataset**: VeriCoder dataset ablation (126k samples)
- **Commit**: ae17392c

## Files

The model includes:
- Model weights in SafeTensors format (5 shards)
- Tokenizer files (tokenizer.json, tokenizer.model, tokenizer_config.json)
- Model configuration (config.json)
- Generation configuration (generation_config.json)
- Chat template (chat_template.jinja)