---
base_model: google/gemma-2-9b-it
datasets:
- nroggendorff/eap
language:
- en
license: mit
tags:
- trl
- sft
- art
- code
- adam
- mistral
model-index:
- name: eap
  results: []
pipeline_tag: text-generation
---

# Edgar Allen Poe LLM

EAP is a language model fine-tuned on the [EAP dataset](https://huggingface.co./datasets/nroggendorff/eap) using Supervised Fine-Tuning (SFT) and Teacher Reinforced Learning (TRL) techniques.

## Features

- Utilizes SFT and TRL techniques for improved performance
- Supports English language

## Usage

To use the LLM, you can load the model using the Hugging Face Transformers library:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

model_id = "nroggendorff/gemma-eap"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)

prompt = "[INST] Write a poem about tomatoes in the style of Poe.[/INST]"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs)

generated_text = tokenizer.batch_decode(outputs)[0]
print(generated_text)
```

## License

This project is licensed under the MIT License.