README.md

metadata

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: []

phi-1_5-finetuned-news-events

This model is a fine-tuned version of 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