---
license: bsd-3-clause
library_name: peft
tags:
- generated_from_trainer
datasets:
- fals3/methods2test_small
metrics:
- accuracy
base_model: Salesforce/codegen-350M-multi
model-index:
- name: output
  results:
  - task:
      type: text-generation
      name: Causal Language Modeling
    dataset:
      name: fals3/methods2test_small fm+fc+c+m+f+t+tc
      type: fals3/methods2test_small
      args: fm+fc+c+m+f+t+tc
    metrics:
    - type: accuracy
      value: 0.6807527762973363
      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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/fals3/methods2test_small/runs/entir0q9)
# output

This model is a fine-tuned version of [Salesforce/codegen-350M-multi](https://huggingface.co/Salesforce/codegen-350M-multi) on the fals3/methods2test_small fm+fc+c+m+f+t+tc dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7674
- Accuracy: 0.6808

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- PEFT 0.10.0
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu118
- Datasets 2.17.1
- Tokenizers 0.19.1