---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/phi-1_5
model-index:
- name: phi-1_5-finetuned-news-events
  results: []
---

<!-- 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. -->

# phi-1_5-finetuned-news-events

This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9322

## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 800

### Training results


### How to use 

```

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Abinaya/phi-1_5-finetuned-news-events", trust_remote_code=True, torch_dtype=torch.float32)

tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True)

```

And test 

```

inputs = tokenizer([f"extract events from news.\n News: {test_data[0]['text']}"], return_tensors="pt", return_attention_mask=False)

outputs = model.generate(**inputs, max_length=512)

text = tokenizer.batch_decode(outputs)[0]

print(text)

```

### Framework versions

- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2