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library_name: transformers |
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base_model: |
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- meta-llama/Llama-3.1-8B |
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# Epos-8B |
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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. |
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- **Developed by:** P0x0 |
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- **Funded by:** P0x0 |
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- **Shared by:** P0x0 |
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- **Model type:** Transformer-based Language Model |
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- **Language(s) (NLP):** Primarily English |
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- **License:** Apache 2.0 |
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- **Finetuned from model:** [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) |
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### Model Sources |
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- **Repository:** [Epos-8B on Hugging Face](https://huggingface.co/P0x0/Epos-8B) |
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- **GGUF:** [GGUF by mradermache](https://huggingface.co/mradermacher/Epos-8b-GGUF) |
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- **imatrix GGUF:**[imatrix quants by mradermacher](https://huggingface.co/mradermacher/Epos-8b-i1-GGUF) |
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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. |
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- **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](https://github.com/LostRuins/koboldcpp), which allows you to run quantized GGUF models locally. |
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### Steps: |
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1. Download [KoboldCPP](https://github.com/LostRuins/koboldcpp). |
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2. Follow the setup instructions provided in the repository. |
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3. Download the GGUF variant of Epos-8B from [Epos-8B-GGUF](https://huggingface.co/P0x0/Epos-8B-GGUF). |
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4. 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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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("P0x0/Epos-8B") |
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model = AutoModelForCausalLM.from_pretrained("P0x0/Epos-8B") |
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input_text = "Once upon a time in a distant land..." |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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