---
library_name: transformers
base_model: adpretko/x86-to-llvm-o2_epoch2
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: x86-to-llvm-o2_epoch3-AMD
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# x86-to-llvm-o2_epoch3-AMD

This model is a fine-tuned version of [adpretko/x86-to-llvm-o2_epoch2](https://huggingface.co/adpretko/x86-to-llvm-o2_epoch2) on the x86-to-llvm-o2_part_00, the x86-to-llvm-o2_part_01, the x86-to-llvm-o2_part_02, the x86-to-llvm-o2_part_03, the x86-to-llvm-o2_part_04, the x86-to-llvm-o2_part_05, the x86-to-llvm-o2_part_06, the x86-to-llvm-o2_part_07, the x86-to-llvm-o2_part_08, the x86-to-llvm-o2_part_09, the x86-to-llvm-o2_part_10, the x86-to-llvm-o2_part_11, the x86-to-llvm-o2_part_12, the x86-to-llvm-o2_part_13, the x86-to-llvm-o2_part_14, the x86-to-llvm-o2_part_15, the x86-to-llvm-o2_part_16, the x86-to-llvm-o2_part_17, the x86-to-llvm-o2_part_18, the x86-to-llvm-o2_part_19, the x86-to-llvm-o2_part_20, the x86-to-llvm-o2_part_21, the x86-to-llvm-o2_part_22, the x86-to-llvm-o2_part_23, the x86-to-llvm-o2_part_24, the x86-to-llvm-o2_part_25, the x86-to-llvm-o2_part_26, the x86-to-llvm-o2_part_27, the x86-to-llvm-o2_part_28, the x86-to-llvm-o2_part_29, the x86-to-llvm-o2_part_30, the x86-to-llvm-o2_part_31, the x86-to-llvm-o2_part_32, the x86-to-llvm-o2_part_33, the x86-to-llvm-o2_part_34, the x86-to-llvm-o2_part_35, the x86-to-llvm-o2_part_36, the x86-to-llvm-o2_part_37, the x86-to-llvm-o2_part_38, the x86-to-llvm-o2_part_39, the x86-to-llvm-o2_part_40, the x86-to-llvm-o2_part_41, the x86-to-llvm-o2_part_42, the x86-to-llvm-o2_part_43, the x86-to-llvm-o2_part_44, the x86-to-llvm-o2_part_45, the x86-to-llvm-o2_part_46, the x86-to-llvm-o2_part_47 and the x86-to-llvm-o2_part_48 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: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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 4.55.0
- Pytorch 2.8.0+rocm6.3
- Datasets 3.6.0
- Tokenizers 0.21.1