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--- |
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license: apache-2.0 |
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base_model: google/gemma-2-12b-it |
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tags: |
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- verilog |
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- code-generation |
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- instruction-tuned |
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- vericoder |
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--- |
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# Gemma-3-12B-IT (VeriCoder Dataset Ablation) |
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This is a fine-tuned version of Gemma-3-12B-IT model trained on VeriCoder dataset. |
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## Model Details |
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- **Base Model**: Gemma-3-12B-IT |
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- **Training Dataset**: VeriCoder dataset (126k samples) |
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- **Model Architecture**: Gemma3ForCausalLM |
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- **Parameters**: ~11.7B |
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- **Context Length**: 131,072 tokens |
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- **Sliding Window**: 1024 |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "LLM4Code/VeriCoder_Gemma12b" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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# Example usage |
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inputs = tokenizer("Your prompt here", return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=512) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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## Training Details |
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- **Dataset**: VeriCoder dataset ablation (126k samples) |
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- **Commit**: ae17392c |
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## Files |
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The model includes: |
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- Model weights in SafeTensors format (5 shards) |
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- Tokenizer files (tokenizer.json, tokenizer.model, tokenizer_config.json) |
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- Model configuration (config.json) |
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- Generation configuration (generation_config.json) |
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- Chat template (chat_template.jinja) |
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