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---
license: llama2
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
- Storywriter
model_type: llama
model-index:
- name: GOAT-70B-Storytelling
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 68.77
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GOAT-AI/GOAT-70B-Storytelling
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 87.74
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GOAT-AI/GOAT-70B-Storytelling
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 69.92
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GOAT-AI/GOAT-70B-Storytelling
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 53.53
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GOAT-AI/GOAT-70B-Storytelling
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 83.5
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GOAT-AI/GOAT-70B-Storytelling
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 40.79
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GOAT-AI/GOAT-70B-Storytelling
name: Open LLM Leaderboard
---
# GOAT-70B-Storytelling model
GOAT-70B-Storytelling model trained by GOAT.AI lab as a core model for an autonomous story-writing agent.
# 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. Examples are provided below.
# Model description
- **Base Architecture:** LLaMA 2 70B
- **License:** llama2
- **Context window length:** 4096 tokens
### Training details
Training was performed on a GPU cluster of 64xH100s. FSDP ZeRO-3 sharding is employed for efficient training. We instruction finetune on a dataset of 18K examples for one epoch with batch size of 336, AdamW optimizer with learning rate 1e-5.
### Learn more
- **Blogpost:** [GOAT-Storytelling: Arbitrarily Long Story Writing Agent](https://www.blog.goat.ai/goat-st/)
- **GitHub:** [here](https://github.com/GOAT-AI-lab/GOAT-Storytelling-Agent)
- **Generated examples:** [here](https://huggingface.co/datasets/GOAT-AI/generated-novels/tree/main/generated-books)
## 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).
Here is how you can utilize the model via GOAT-Storytelling-Agent:
```python
from goat_storytelling_agent.storytelling_agent import StoryAgent
backend_uri = # Text generation endpoint
writer = StoryAgent(backend_uri, form='novel')
novel_scenes = writer.generate_story('treasure hunt in a jungle')
```
## 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.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_GOAT-AI__GOAT-70B-Storytelling)
| Metric |Value|
|---------------------------------|----:|
|Avg. |67.38|
|AI2 Reasoning Challenge (25-Shot)|68.77|
|HellaSwag (10-Shot) |87.74|
|MMLU (5-Shot) |69.92|
|TruthfulQA (0-shot) |53.53|
|Winogrande (5-shot) |83.50|
|GSM8k (5-shot) |40.79|
|