README.md

---
license: apache-2.0
language:
- en
base_model:
- PygmalionAI/Eleusis-12B
pipeline_tag: text-generation
---

This is an ONNX optimized version of [Eleusis-12B](https://huggingface.co/PygmalionAI/Eleusis-12B).
For a more comprehensive info about the model's capabilities, please visit the original model's repo.

## Inference
### Requirements
If you're on a CPU-only machine:

```sh
pip install onnxruntime
```

If you have an NVIDIA GPU available:
```sh
pip uninstall onnxruntime -y
pip install onnxruntime-gpu
```

Make sure you have installed [CUDA Toolkit](https://developer.nvidia.com/cuda-12-4-0-download-archive) and [cuDNN](https://developer.nvidia.com/cudnn)

```sh
import onnxruntime as ort
from transformers import AutoTokenizer
import numpy as np
import argparse

def generate_text(prompt, num_tokens, model_path, tokenizer_path):
    tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
    session = ort.InferenceSession(model_path, providers=providers)

    input_ids = tokenizer(prompt, return_tensors="np").input_ids

    for _ in range(num_tokens):
        # Create attention mask and position ids
        attention_mask = np.ones_like(input_ids)
        position_ids = np.arange(input_ids.shape[1])[None, :]

        outputs = session.run(
            output_names=['logits'],
            input_feed={
                'input_ids': input_ids,
                'attention_mask': attention_mask,
                'position_ids': position_ids
            }
        )

        next_token = np.argmax(outputs[0][0, -1, :])

        input_ids = np.concatenate([input_ids, [[next_token]]], axis=1)

    return tokenizer.decode(input_ids[0], skip_special_tokens=True)

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Generate text using ONNX model')
    parser.add_argument('prompt', type=str, help='Input prompt for generation')
    parser.add_argument('num_tokens', type=int, help='Number of tokens to generate')
    parser.add_argument('--model_path', type=str, default='model.onnx', 
                        help='Path to ONNX model file')
    parser.add_argument('--tokenizer_path', type=str, default='tokenizer',
                        help='Path to tokenizer directory')

    args = parser.parse_args()

    result = generate_text(args.prompt, args.num_tokens, args.model_path, args.tokenizer_path)
    print(result)
```

```sh
python onnx_inference.py "Once upon a time" 512 --model_path /path/to/model.onnx --tokenizer_path /path/to/model/dir
```

This is an example script, and not properly optimized.