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README.md

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+---
+license: llama2
+model_type: llama
+tags:
+  - facebook
+  - meta
+  - pytorch
+  - llama
+  - llama-2
+  - Storywriter
+---
+
+![GOAT-70B-Storytelling](https://assets.adapt.ws/files/20231117_ehznrqludevtapck.png)
+# 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)
+
+First, modify `config.py` and add your generation endpoint.
+
+Then you can use it inside via GOAT-Storytelling-Agent:
+
+```python
+from goat_storytelling_agent import storytelling_agent as goat
+
+novel_scenes = goat.generate_story('treasure hunt in a jungle', 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.

config.json

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+{
+  "_name_or_path": "/mnt/data1/models/tolkien/v0_0_5",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": 28672,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 80,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.34.1",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "max_length": 4096,
+  "pad_token_id": 0,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.34.1"
+}

huggingface-metadata.txt

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