README.md

---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-14B
tags:
- generated_from_trainer
datasets:
- andstor/methods2test_small
metrics:
- accuracy
model-index:
- name: output
  results:
  - task:
      type: text-generation
      name: Causal Language Modeling
    dataset:
      name: andstor/methods2test_small fm+fc+c+m+f+t+tc
      type: andstor/methods2test_small
      args: fm+fc+c+m+f+t+tc
    metrics:
    - type: accuracy
      value: 0.7337136614077643
      name: Accuracy
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# output

This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-14B](https://huggingface.co/Qwen/Qwen2.5-Coder-14B) on the andstor/methods2test_small fm+fc+c+m+f+t+tc dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7060
- Accuracy: 0.7337

## 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: 0.0003
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 2
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Training results



### Framework versions

- PEFT 0.15.2
- Transformers 4.56.2
- Pytorch 2.8.0+cu128
- Datasets 4.1.1
- Tokenizers 0.22.1