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---
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
should probably proofread and complete it, then remove this comment. -->
# 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 |