metadata

library_name: peft
base_model: meta-llama/Llama-2-7b-hf
license: mit
language:
  - en
metrics:
  - bertscore
  - perplexity

Model Card for Model ID

Fine-tuned using QLoRA for story generation task.

We utilize "Hierarchical Neural Story Generation" dataset and fine-tune the model to generate stories.

The input to the model is structred as follows:

'''

### Instruction: Below is a story idea. Write a short story based on this context.

### Input: [story idea here]

### Response:

'''

Developed by: Abdelrahman ’Boda’ Sadallah, Anastasiia Demidova, Daria Kotova
Model type: Causal LM
Language(s) (NLP): English
Finetuned from model [optional]: meta-llama/Llama-2-7b-hf

Uses

The model is the result of our AI project. If you intend to use it, please, refer to the repo.

For improving stories generation, you can play parameters: temeperature, top_p/top_k, repetition_penalty, etc.

Use the code below to get started with the model.

[More Information Needed]

[More Information Needed]

Test split of the same dataset.

We are using perplexity and BERTScore.

Perplexity: 8.0546

BERTScore: 80.11

[More Information Needed]

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

The following bitsandbytes quantization config was used during training: