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---
license: llama2
model_type: llama
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
- Storywriter
---
# GOAT-70B-Storytelling model
GOAT-70B-Storytelling model trained by GOAT.AI lab as a core model for autonomous story-writing agent.
# GOAT-Storytelling-Agent
The GOAT-70B-Storytelling model has been developed as an integral component within the GOAT-Storytelling-Agent. This agent facilitates the generation of high-quality, cohesive, and captivating narratives, including stories and books. It achieves this by utilizing inputs such as plot outlines, character profiles, their interrelationships, and other relevant details. Example is provided below.
# Model description
- **Base Architecture:** LLaMA 2 70B
- **License:** llama2
- **Context window length:** 4096 tokens
### Training details
For training, we apply the standard recipe with learning rate 1e-5, batch size per GPU 6, optimizer AdamW without weight decay and we train the model via ZeRO-3 on 64xH100 GPU cluster
### Learn more
- **Blogpost:** [GOAT-Storytelling: Arbitrarily Long Story Writing Agent](https://www.blog.goat.ai/goat-st/)
- **GitHub:** [here](github.com/GOAT-AI-lab/GOAT-Storytelling-Agent)
- **Generated examples:** [here](https://huggingface.co/datasets/GOAT-AI/generated-novels)
## Uses
The main purpose of GOAT-70B-Storytelling is to generate books, novels, movie scripts and etc. as an agent in coping with our GOAT-Storytelling-Agent. It is specifically designed for storywriters.
## Usage
Usage can be either self-hosted via `transformers` or used with Spaces
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "GOAT-AI/GOAT-70B-Storytelling"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16
)
```
Currently, we support LLM endpoint generation, where you need to send a post request to the generation endpoint (we recommend using Text Generation Inference by HuggingFace)
First, modify config.py and add your generation endpoint.
Then you can use it inside via GOAT-Storytelling-Agent:
```python
from goat_storytelling_agent.story_processor.prompt_manager import generate_story
novel_scenes = generate_story('never too much coffee', form='novel')
```
## License
GOAT-70B-Storytelling model is based on [Meta's LLaMA-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf), and using own datasets.
GOAT-70B-Storytelling model weights are available under LLAMA-2 license.
### Risks and Biases
GOAT-70B-Storytelling model can produce factually incorrect output and should not be relied on to deliver factually accurate information. Therefore, the GOAT-70B-Storytelling model could possibly generate wrong, biased, or otherwise offensive outputs. |