LLM4Code/VeriCoder_Gemma12b

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

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)