trainedaktor

Introduction

The trainedaktor model, developed by thepinkdrummer, is designed for text generation tasks. It was trained using the AutoTrain platform and supports various use cases such as text-generation-inference and conversational applications. The model leverages the Transformers library and is built upon the meta-llama/Llama-3.2-1B base model.

Architecture

The model is a part of the Transformers library, specifically designed for causal language modeling tasks. It utilizes the meta-llama/Llama-3.2-1B as its base, ensuring it benefits from a robust architecture suitable for generating coherent text responses.

Training

Training was conducted using the AutoTrain platform, which simplifies the training process by automating many of the complex steps involved. AutoTrain allows for efficient fine-tuning and optimization of models on specific datasets, in this case, the dataset thepinkdrummer/ruggedaktor.

Guide: Running Locally

To run the trainedaktor model locally, follow these steps:

1. Install Required Libraries: Ensure you have the Transformers library installed. You can install it using pip:

   pip install transformers
   

2. Download the Model: Clone the repository or download the model files from the Hugging Face Hub.

3. Load the Model and Tokenizer:

   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   model_path = "PATH_TO_THIS_REPO"
   tokenizer = AutoTokenizer.from_pretrained(model_path)
   model = AutoModelForCausalLM.from_pretrained(
       model_path,
       device_map="auto",
       torch_dtype='auto'
   ).eval()
   

4. Generate Text: Use the model to generate text responses. Make sure your environment supports CUDA for optimal performance, or use a cloud GPU service like AWS, Google Cloud, or Azure.

   messages = [{"role": "user", "content": "hi"}]
   input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
   output_ids = model.generate(input_ids.to('cuda'))
   response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
   print(response)
   

License

The trainedaktor model is released under an "other" license. Users should refer to the model's page on the Hugging Face Hub for specific licensing details and restrictions.