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README.md

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 This model was converted to GGUF format from [`P0x0/Epos-8b`](https://huggingface.co/P0x0/Epos-8b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/P0x0/Epos-8b) for more details on the model.
 
+---
+Model details:
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+Epos-8B is a fine-tuned version of the base model Llama-3.1-8B
+ from Meta, optimized for storytelling, dialogue generation, and 
+creative writing. The model specializes in generating rich narratives, 
+immersive prose, and dynamic character interactions, making it ideal for
+ creative tasks.
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+		Model Details
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+		Model Description
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+Epos-8B is an 8 billion parameter language model fine-tuned for 
+storytelling and narrative tasks. Inspired by the grandeur of epic 
+tales, it is designed to produce high-quality, engaging content that 
+evokes the depth and imagination of ancient myths and modern 
+storytelling traditions.
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+Developed by: P0x0
+Funded by: P0x0
+Shared by: P0x0
+Model type: Transformer-based Language Model
+Language(s) (NLP): Primarily English
+License: Apache 2.0
+Finetuned from model: meta-llama/Llama-3.1-8B
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+		Model Sources
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+Repository: Epos-8B on Hugging Face
+GGUF Repository: Epos-8B-GGUF (TO BE ADDED)
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+		Uses
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+		Direct Use
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+Epos-8B is ideal for:
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+Storytelling: Generate detailed, immersive, and engaging narratives.
+Dialogue Creation: Create realistic and dynamic character interactions for stories or games.
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+		How to Get Started with the Model
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+To run the quantized version of the model, you can use KoboldCPP, which allows you to run quantized GGUF models locally.
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+		Steps:
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+Download KoboldCPP.
+Follow the setup instructions provided in the repository.
+Download the GGUF variant of Epos-8B from Epos-8B-GGUF.
+Load the model in KoboldCPP and start generating!
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+Alternatively, integrate the model directly into your code with the following snippet:
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+from transformers import AutoModelForCausalLM, AutoTokenizer
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+tokenizer = AutoTokenizer.from_pretrained("P0x0/Epos-8B")
+model = AutoModelForCausalLM.from_pretrained("P0x0/Epos-8B")
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+input_text = "Once upon a time in a distant land..."
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
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+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)