---
library_name: transformers
base_model:
- meta-llama/Llama-3.1-8B
---

# Epos-8B

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.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65dbd5a60e6ad24551b3959f/P01YmhjrdTfpJBpyWfyy9.png)

---

 
## Model Details

### Model Description

Epos-8B is an 8 billion parameter language model fine-tuned for storytelling and narrative tasks.
- **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](https://huggingface.co/meta-llama/Llama-3.1-8B)

### Model Sources

- **Repository:** [Epos-8B on Hugging Face](https://huggingface.co/P0x0/Epos-8B)
- **GGUF:** [GGUF by mradermache](https://huggingface.co/mradermacher/Epos-8b-GGUF)
- **imatrix GGUF:**[imatrix quants by mradermacher](https://huggingface.co/mradermacher/Epos-8b-i1-GGUF)
---

## 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](https://github.com/LostRuins/koboldcpp), which allows you to run quantized GGUF models locally.

### Steps:
1. Download [KoboldCPP](https://github.com/LostRuins/koboldcpp).
2. Follow the setup instructions provided in the repository.
3. Download the GGUF variant of Epos-8B from [Epos-8B-GGUF](https://huggingface.co/P0x0/Epos-8B-GGUF).
4. Load the model in KoboldCPP and start generating!

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

```python
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))