---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/Phi-3-mini-4k-instruct
datasets:
- generator
model-index:
- name: cls_fomc_phi3_v1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# cls_fomc_phi3_v1

This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7320

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8109        | 0.3883 | 20   | 0.7927          |
| 0.7639        | 0.7767 | 40   | 0.7570          |
| 0.6942        | 1.1650 | 60   | 0.7449          |
| 0.6797        | 1.5534 | 80   | 0.7417          |
| 0.6899        | 1.9417 | 100  | 0.7320          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1