---
library_name: transformers
base_model: P0x0/Epos-8b
tags:
- llama-cpp
- gguf-my-repo
license: llama3.1
---

# Triangle104/Epos-8b-Q4_K_S-GGUF
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:
-
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.







	
		
	

		Model Details
	




	
		
	

		Model Description
	



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.


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



	
		
	

		Model Sources
	



Repository: Epos-8B on Hugging Face
GGUF Repository: Epos-8B-GGUF (TO BE ADDED)




	
		
	

		Uses
	




	
		
	

		Direct Use
	



Epos-8B is ideal for:


Storytelling: Generate detailed, immersive, and engaging narratives.
Dialogue Creation: Create realistic and dynamic character interactions for stories or games.



	
		
	

		How to Get Started with the Model
	



To run the quantized version of the model, you can use KoboldCPP, which allows you to run quantized GGUF models locally.



	
		
	

		Steps:
	



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!


Alternatively, integrate the model directly into your code with the following snippet:


from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("P0x0/Epos-8B")
model = AutoModelForCausalLM.from_pretrained("P0x0/Epos-8B")

input_text = "Once upon a time in a distant land..."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Epos-8b-Q4_K_S-GGUF --hf-file epos-8b-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Epos-8b-Q4_K_S-GGUF --hf-file epos-8b-q4_k_s.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Epos-8b-Q4_K_S-GGUF --hf-file epos-8b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Epos-8b-Q4_K_S-GGUF --hf-file epos-8b-q4_k_s.gguf -c 2048
```