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--- |
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license: llama2 |
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model_type: llama |
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tags: |
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- facebook |
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- meta |
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- pytorch |
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- llama |
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- llama-2 |
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- Storywriter |
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--- |
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# GOAT-70B-Storytelling model |
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GOAT-70B-Storytelling model trained by GOAT.AI lab as a core model for an autonomous story-writing agent. |
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# GOAT-Storytelling-Agent |
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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. |
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# Model description |
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- **Base Architecture:** LLaMA 2 70B |
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- **License:** llama2 |
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- **Context window length:** 4096 tokens |
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### Training details |
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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. |
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### Learn more |
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- **Blogpost:** [GOAT-Storytelling: Arbitrarily Long Story Writing Agent](https://www.blog.goat.ai/goat-st/) |
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- **GitHub:** [here](https://github.com/GOAT-AI-lab/GOAT-Storytelling-Agent) |
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- **Generated examples:** [here](https://huggingface.co/datasets/GOAT-AI/generated-novels/tree/main/generated-books) |
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## Uses |
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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. |
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## Usage |
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Usage can be either self-hosted via `transformers` or used with Spaces |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_name = "GOAT-AI/GOAT-70B-Storytelling" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.bfloat16 |
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) |
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``` |
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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) |
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First, modify `config.py` and add your generation endpoint. |
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Then you can use it inside via GOAT-Storytelling-Agent: |
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```python |
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from goat_storytelling_agent import storytelling_agent as goat |
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novel_scenes = goat.generate_story('treasure hunt in a jungle', form='novel') |
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``` |
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## License |
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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. |
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GOAT-70B-Storytelling model weights are available under LLAMA-2 license. |
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### Risks and Biases |
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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. |
