prithivMLmods/Llama-3.2-3B-Promptist-Mini

Llama-3.2-3B-Promptist-Mini Model Files

The Llama-3.2-3B-Promptist-Mini is a fine-tuned version of the Llama-3.2-3B-Instruct model, specifically optimized for prompt engineering and enhancement tasks. It is ideal for generating and enhancing various types of text prompts, offering high performance in creative and instructional applications. The model leverages a smaller, more efficient architecture suited for fine-tuning and prompt-based use cases.

File Name	Size	Description	Upload Status
.gitattributes	1.52 kB	Git attributes configuration file	Uploaded
README.md	287 Bytes	Updated README file	Updated
config.json	940 Bytes	Model configuration settings	Uploaded
generation_config.json	162 Bytes	Generation-specific configurations	Uploaded
merges.txt	515 kB	Merging information for tokenization	Uploaded
pytorch_model.bin	3.42 GB	Full model weights (PyTorch format)	Uploaded (LFS)
special_tokens_map.json	572 Bytes	Mapping for special tokens used by the model	Uploaded
tokenizer.json	3.77 MB	Tokenizer configuration and vocabulary	Uploaded
tokenizer_config.json	3.95 kB	Tokenizer configuration for loading and usage	Uploaded
vocab.json	801 kB	Vocabulary for the tokenizer	Uploaded

Key Features:

1. Prompt Engineering and Enhancement:
   This model is fine-tuned to generate and improve prompts for various applications, such as question generation, creative writing, and instruction-following tasks.

2. Text Generation:
   It excels in generating coherent and contextually relevant text based on the given prompts. The model can be used for a wide range of text-based applications, including content creation and automated text generation.

3. Custom Tokenizer:
   Includes a tokenizer optimized for handling specialized tokens related to prompt-based tasks, ensuring the model performs well in generating creative and logical text.

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Training Details:

• Base Model: Llama-3.2-3B-Instruct
• Dataset: Trained on Prompt-Enhancement-Mini, a dataset specifically designed to enhance prompt generation, with examples tailored to creative and instructional contexts.

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Capabilities:

• Prompt Generation and Enhancement:
  The model can generate and enhance prompts for various tasks, including machine learning, creative writing, and instructional content.

• Text Generation:
  It excels at generating coherent, structured, and contextually appropriate text from user inputs, making it suitable for a wide variety of text-based applications.

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Usage Instructions:

1. Model Setup: Download all model files and ensure the PyTorch model weights and tokenizer configurations are included.
2. Inference: Load the model in a Python environment using frameworks like PyTorch or Hugging Face's Transformers.
3. Customization: Configure the model with the config.json and generation_config.json files for optimal performance during inference.

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