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