pytorch llama region:us
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# VivekaLM

![VivekaLM](https://huggingface.co/ramanandr/VivekaLM/resolve/main/logo.png) <!-- Replace with your model logo -->

## Model Description

**VivekaLM** is a language model based on the LLaMA architecture, fine-tuned for [describe the task, e.g., natural language understanding, text generation, etc.]. It aims to [describe the model's purpose, e.g., assist in generating coherent text, answering questions, etc.].

## Model Details

- **Model Type**: Causal Language Model
- **Architecture**: LLaMA
- **Pretrained on**: [describe the dataset used for pretraining]
- **Fine-tuned on**: [describe any specific dataset or task if applicable]
- **Intended Use**: [describe the primary use cases for the model, e.g., chatbot, summarization, etc.]

## How to Use

To use the model for inference, you can use the following code snippet:

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

# Load the model and tokenizer
model_name = "ramanandr/VivekaLM"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Move model to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Prepare input
input_text = "Your input text here"
input_ids = tokenizer.encode(input_text, return_tensors='pt').to(device)

# Generate response
with torch.no_grad():
    outputs = model.generate(input_ids, max_length=50)
    
# Decode the response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

## Training and Evaluation

- **Training Dataset**: [Specify the datasets used for training]
- **Training Procedure**: [Briefly describe how the model was trained, including parameters, epochs, and any special techniques]
- **Evaluation Metrics**: [List any metrics used to evaluate the model, e.g., perplexity, accuracy, etc.]

## Limitations

- The model may not generalize well to domains or topics that were not well-represented in the training data.
- [List any other limitations, such as biases, potential ethical concerns, etc.]

## Disclaimer

This model is provided "as is" without warranty of any kind. Users are responsible for ensuring that the model is used ethically and in accordance with applicable laws.

## License

[Specify the license under which the model is released, e.g., MIT, Apache 2.0, etc.]

## Citation

If you use this model in your work, please cite it as follows:

```bibtex
@misc{VivekaLM,
  author = {Ramanand R.},
  title = {VivekaLM: A LLaMA-Based Language Model},
  year = {2024},
  url = {https://huggingface.co/ramanandr/VivekaLM}
}
```

## Contact

For further questions, please contact [your email or any contact method].

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### Customization Tips:

- **Logo**: Replace the logo URL with your own logo if you have one.
- **Task and Dataset Descriptions**: Fill in the sections related to the model's training data and intended use more specifically based on what you've done with the model.
- **License**: Clearly state the licensing terms.
- **Contact Information**: Provide a way for users to reach out with questions or feedback.