---
library_name: peft
base_model: ericzzz/falcon-rw-1b-instruct-openorca
license: apache-2.0
language:
- en
---

## Model Description

Quick and dirty hack for binary movie sentiment analysis.

Finetuned with LoRA (PEFT) on [ericzzz/falcon-rw-1b-instruct-openorca](https://huggingface.co/datasets/open-llm-leaderboard/details_ericzzz__falcon-rw-1b-instruct-openorca).

Trained on a subset of [IMDB Dataset of 50K Movie Reviews](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews) from Kaggle:

**To load the model you can use this code:**

    PEFT_MODEL = "Jonny00/falcon-1b-movie-sentiment-analysis"
    
    config = PeftConfig.from_pretrained(PEFT_MODEL)
    model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    return_dict=True,
    device_map="auto",
    trust_remote_code=True)
    
    tokenizer=AutoTokenizer.from_pretrained(config.base_model_name_or_path)
    tokenizer.pad_token = tokenizer.eos_token
    
    model = PeftModel.from_pretrained(model, PEFT_MODEL)

**Input**: *("\<human\>: This movie sucks, I'd rather stay at home! \<assistant\>:")*

**Output**: *("... negative \<assistant\>: negative \<assistant\>: negative ...")*

## Example Google Colab Code

https://colab.research.google.com/drive/1LUILztSocpqpMz8xACbtmxl-W-cORXRZ?usp=sharing

## Framework versions

- PEFT 0.7.1