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@@ -10,15 +10,15 @@ tags:
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  - Storywriter
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  ---
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- # GOAT-70B-STORYTELLING model
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- ![GOAT-70B-STORYTELLING](https://assets.adapt.ws/files/20231117_ehznrqludevtapck.png)
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  GOAT-70B-Storytelling model developed by GOAT.AI lab for autonomous story-writing.
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- # GOAT-STORYTELLING-AGENT
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- The GOAT-70B-STORYTELLING model has been developed as an integral component within the GOAT-STORYTELLING-AGENT Framework. This framework is designed to facilitate 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.
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  Generated examples can be accessed [here](https://huggingface.co/datasets/GOAT-AI/generated-novels)
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  # Model description
@@ -29,10 +29,10 @@ Generated examples can be accessed [here](https://huggingface.co/datasets/GOAT-A
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  ### Learn more
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  - **Blog:** TBA
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- - **Framework:** github.com/GOAT-STORYTELLING-AGENT (TBA)
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  ## Uses
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- The main purpose of GOAT-70B-STORYTELLING is to generate books, novels, moviescripts and etc. as an agent in cope with our GOAT-STORYTELLING-AGENT Framework. It is specifically designed for storywriters.
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  ## Usage
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@@ -44,7 +44,7 @@ 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(
@@ -52,11 +52,11 @@ model = AutoModelForCausalLM.from_pretrained(
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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 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 framework:
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  ```python
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  from goat_storytelling_agent.story_processor.prompt_manager import generate_story
@@ -65,10 +65,10 @@ novel_scenes = generate_story('never too much coffee', 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. Note that the GOAT-70B-STORYTELLING model weights require access to the LLaMA-2 model weighs. The GOAT-70B-STORYTELLING model is based on LLaMA-2 and should be used according to the 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.
 
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  - Storywriter
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  ---
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+ # GOAT-70B-Storytelling model
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+ ![GOAT-70B-Storytelling](https://assets.adapt.ws/files/20231117_ehznrqludevtapck.png)
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  GOAT-70B-Storytelling model developed by GOAT.AI lab for autonomous story-writing.
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+ # GOAT-Storytelling-Agent
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+ 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.
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  Generated examples can be accessed [here](https://huggingface.co/datasets/GOAT-AI/generated-novels)
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  # Model description
 
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  ### Learn more
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  - **Blog:** TBA
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+ - **GitHub:** github.com/GOAT-Storytelling-Agent (TBA)
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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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  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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  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.story_processor.prompt_manager import generate_story
 
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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. Note that the GOAT-70B-Storytelling model weights require access to the LLaMA-2 model weighs. The GOAT-70B-Storytelling model is based on LLaMA-2 and should be used according to the 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.