prithivMLmods/Novaeus-Promptist-7B-Instruct-GGUF

Novaeus-Promptist-7B-Instruct Uploaded Model Files

The Novaeus-Promptist-7B-Instruct is a fine-tuned large language model derived from the Qwen2.5-7B-Instruct base model. It is optimized for prompt enhancement, text generation, and instruction-following tasks, providing high-quality outputs tailored to various applications.

File Name	Size	Description	Upload Status
.gitattributes	1.83 kB	Git attributes for handling LFS.	Uploaded
Novaeus-Promptist-7B-Instruct.F16.gguf	15.2 GB	Full precision model weights (FP16).	Uploaded (LFS)
Novaeus-Promptist-7B-Instruct.Q4_K_M.gguf	4.68 GB	Quantized weights for Q4 (K_M).	Uploaded (LFS)
Novaeus-Promptist-7B-Instruct.Q5_K_M.gguf	5.44 GB	Quantized weights for Q5 (K_M).	Uploaded (LFS)
Novaeus-Promptist-7B-Instruct.Q8_0.gguf	8.1 GB	Quantized weights for Q8.	Uploaded (LFS)
README.md	388 Bytes	Model overview and usage documentation.	Updated
config.json	29 Bytes	Configuration for the model.	Uploaded

---

Key Features:

1. Prompt Refinement:
   Designed to enhance input prompts by rephrasing, clarifying, and optimizing for more precise outcomes.

2. Instruction Following:
   Accurately follows complex user instructions for various generation tasks, including creative writing, summarization, and question answering.

3. Customization and Fine-Tuning:
   Incorporates datasets specifically curated for prompt optimization, enabling seamless adaptation to specific user needs.

---

Training Details:

• Base Model: Qwen2.5-7B-Instruct
• Datasets Used for Fine-Tuning:
  • gokaygokay/prompt-enhancer-dataset: Focuses on prompt engineering with 17.9k samples.
  • gokaygokay/prompt-enhancement-75k: Encompasses a wider array of prompt styles with 73.2k samples.
  • prithivMLmods/Prompt-Enhancement-Mini: A compact dataset (1.16k samples) for iterative refinement.

---

Capabilities:

• Prompt Optimization:
  Automatically refines and enhances user-input prompts for better generation results.

• Instruction-Based Text Generation:
  Supports diverse tasks, including:

  • Creative writing (stories, poems, scripts).
  • Summaries and paraphrasing.
  • Custom Q&A systems.

• Efficient Fine-Tuning:
  Adaptable to additional fine-tuning tasks by leveraging the model's existing high-quality instruction-following capabilities.

---

Usage Instructions:

1. Setup:

   • Ensure all necessary model files, including shards, tokenizer configurations, and index files, are downloaded and placed in the correct directory.

2. Load Model:
   Use PyTorch or Hugging Face Transformers to load the model and tokenizer. Ensure pytorch_model.bin.index.json is correctly set for efficient shard-based loading.

3. Customize Generation:
   Adjust parameters in generation_config.json to control aspects such as temperature, top-p sampling, and maximum sequence length.

---

Run with Ollama [ Ollama Run ]

Overview

Ollama is a powerful tool that allows you to run machine learning models effortlessly. This guide will help you download, install, and run your own GGUF models in just a few minutes.

Table of Contents

• Download and Install Ollama
• Steps to Run GGUF Models
  • 1. Create the Model File
  • 2. Add the Template Command
  • 3. Create and Patch the Model
• Running the Model
• Sample Usage

Download and Install Ollama🦙

To get started, download Ollama from https://ollama.com/download and install it on your Windows or Mac system.

Steps to Run GGUF Models

1. Create the Model File

First, create a model file and name it appropriately. For example, you can name your model file metallama.

2. Add the Template Command

In your model file, include a FROM line that specifies the base model file you want to use. For instance:

FROM Llama-3.2-1B.F16.gguf

Ensure that the model file is in the same directory as your script.

3. Create and Patch the Model

Open your terminal and run the following command to create and patch your model:

ollama create metallama -f ./metallama

Once the process is successful, you will see a confirmation message.

To verify that the model was created successfully, you can list all models with:

ollama list

Make sure that metallama appears in the list of models.

---

Running the Model

To run your newly created model, use the following command in your terminal:

ollama run metallama

Sample Usage / Test

In the command prompt, you can execute:

D:\>ollama run metallama

You can interact with the model like this:

>>> write a mini passage about space x
Space X, the private aerospace company founded by Elon Musk, is revolutionizing the field of space exploration.
With its ambitious goals to make humanity a multi-planetary species and establish a sustainable human presence in
the cosmos, Space X has become a leading player in the industry. The company's spacecraft, like the Falcon 9, have
demonstrated remarkable capabilities, allowing for the transport of crews and cargo into space with unprecedented
efficiency. As technology continues to advance, the possibility of establishing permanent colonies on Mars becomes
increasingly feasible, thanks in part to the success of reusable rockets that can launch multiple times without
sustaining significant damage. The journey towards becoming a multi-planetary species is underway, and Space X
plays a pivotal role in pushing the boundaries of human exploration and settlement.

---

Conclusion

With these simple steps, you can easily download, install, and run your own models using Ollama. Whether you're exploring the capabilities of Llama or building your own custom models, Ollama makes it accessible and efficient.

• This README provides clear instructions and structured information to help users navigate the process of using Ollama effectively. Adjust any sections as needed based on your specific requirements or additional details you may want to include.