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VeriCoder_Gemma12b – AI Model by LLM4Code | AlphaNeural AI

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LLM4Code/
VeriCoder_Gemma12b

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instruction-tuned

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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)