| --- |
| 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) |
| |
| |
