--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: x86-to-llvm-o0 results: [] --- # x86-to-llvm-o0 This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) on the x86-to-llvm-o0_part_00, the x86-to-llvm-o0_part_01, the x86-to-llvm-o0_part_02, the x86-to-llvm-o0_part_03, the x86-to-llvm-o0_part_04, the x86-to-llvm-o0_part_05, the x86-to-llvm-o0_part_06, the x86-to-llvm-o0_part_07, the x86-to-llvm-o0_part_08, the x86-to-llvm-o0_part_09, the x86-to-llvm-o0_part_10, the x86-to-llvm-o0_part_11, the x86-to-llvm-o0_part_12, the x86-to-llvm-o0_part_13, the x86-to-llvm-o0_part_14, the x86-to-llvm-o0_part_15, the x86-to-llvm-o0_part_16, the x86-to-llvm-o0_part_17, the x86-to-llvm-o0_part_18, the x86-to-llvm-o0_part_19, the x86-to-llvm-o0_part_20, the x86-to-llvm-o0_part_21, the x86-to-llvm-o0_part_22, the x86-to-llvm-o0_part_23, the x86-to-llvm-o0_part_24, the x86-to-llvm-o0_part_25, the x86-to-llvm-o0_part_26, the x86-to-llvm-o0_part_27, the x86-to-llvm-o0_part_28, the x86-to-llvm-o0_part_29, the x86-to-llvm-o0_part_30, the x86-to-llvm-o0_part_31, the x86-to-llvm-o0_part_32, the x86-to-llvm-o0_part_33, the x86-to-llvm-o0_part_34, the x86-to-llvm-o0_part_35, the x86-to-llvm-o0_part_36, the x86-to-llvm-o0_part_37, the x86-to-llvm-o0_part_38, the x86-to-llvm-o0_part_39, the x86-to-llvm-o0_part_40, the x86-to-llvm-o0_part_41, the x86-to-llvm-o0_part_42, the x86-to-llvm-o0_part_43, the x86-to-llvm-o0_part_44, the x86-to-llvm-o0_part_45 and the x86-to-llvm-o0_part_46 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.52.1 - Pytorch 2.8.0+rocm6.3 - Datasets 3.6.0 - Tokenizers 0.21.1