---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- JunxiongWang/sftdataset
model-index:
- name: 1b_distill_width_prune
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 1b_distill_width_prune

This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on the JunxiongWang/sftdataset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5712

## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 4
- 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: 0.01
- training_steps: 20000

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 1.3657        | 0.0413 | 10000 | 1.7362          |
| 1.2947        | 0.0827 | 20000 | 1.5713          |


### Framework versions

- Transformers 4.47.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.20.0
- Tokenizers 0.20.3