README.md

---
language:
- en
tags:
- text-generation-inference
---

# Model Card for Mistral-7B for Story Generation

### Model Description

<!-- Provide a longer summary of what this model is. -->

This model is a fine-tuned **Mistral-7B** model on stories from the [WritingPrompts dataset](https://huggingface.co/datasets/euclaise/writingprompts).

- **Language(s) (NLP):** English
- **Finetuned from model:** [m-elio/Mistral-BookCorpus-Gutenberg](https://huggingface.co/m-elio/Mistral-BookCorpus-Gutenberg)
- **Dataset used for fine-tuning:** [WritingPrompts](https://huggingface.co/datasets/euclaise/writingprompts)


### Example of Usage

```python
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.trainer_utils import set_seed

set_seed(42)

model_id = "m-elio/Mistral-BookCorpus-Gutenberg-Writing-Prompts"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

instruction_text = "Write a story for the writing prompt provided as input"
input_text = "A story about a dancer who tries to win the National championship."

prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n" \
        f"### Instruction:\nWrite a story for the writing prompt provided as input\n\n" \
        f"### Input:\n{input_text}\n\n" \
        f"### Answer:\n"

input_ids = tokenizer(prompt, return_tensors="pt").input_ids
outputs = model.generate(input_ids=input_ids, top_k=0, top_p=0.92, do_sample=True, max_new_tokens=2048)

print(tokenizer.batch_decode(outputs.detach().cpu().numpy()[:, input_ids.shape[1]:], skip_special_tokens=True)[0])
```