QuantFactory/Codepy-Deepthink-3B-GGUF

This is quantized version of prithivMLmods/Codepy-Deepthink-3B created using llama.cpp

Original Model Card

Codepy 3B Deep Think Model File

The Codepy 3B Deep Think Model is a fine-tuned version of the meta-llama/Llama-3.2-3B-Instruct base model, designed for text generation tasks that require deep reasoning, logical structuring, and problem-solving. This model leverages its optimized architecture to provide accurate and contextually relevant outputs for complex queries, making it ideal for applications in education, programming, and creative writing.

With its robust natural language processing capabilities, Codepy 3B Deep Think excels in generating step-by-step solutions, creative content, and logical analyses. Its architecture integrates advanced understanding of both structured and unstructured data, ensuring precise text generation aligned with user inputs.

Model Content	Size	Description	Upload Status
.gitattributes	1.57 kB	Git LFS configuration for large files.	Uploaded
README.md	221 Bytes	Basic repository information.	Updated
config.json	994 Bytes	Model configuration with architectural details.	Uploaded
generation_config.json	248 Bytes	Default generation parameters.	Uploaded
pytorch_model-00001-of-00002.bin	4.97 GB	Sharded PyTorch model weights (part 1 of 2).	Uploaded (LFS)
pytorch_model-00002-of-00002.bin	1.46 GB	Sharded PyTorch model weights (part 2 of 2).	Uploaded (LFS)
pytorch_model.bin.index.json	21.2 kB	Index file mapping model shards.	Uploaded
special_tokens_map.json	477 Bytes	Maps special tokens to their respective IDs.	Uploaded
tokenizer.json	17.2 MB	Full tokenizer vocabulary and merges.	Uploaded (LFS)
tokenizer_config.json	57.5 kB	Tokenizer configuration details.	Uploaded

Run with LM Studio	Details
Run with LM Studio	https://lmstudio.ai/
Demo on LM Studio	https://drive.google.com/file/d/1CHdfjYrwMnk9ACvS40Abfy3xNXnCubKG/view?usp=sharing
Codepy-Deepthink-3B-GGUF	https://huggingface.co/prithivMLmods/Codepy-Deepthink-3B-GGUF

---

Sample Deepthink Inference

Develop a Python program to generate random passwords that consist of 8 characters. Not applicable

# Random Password Generator

This Python program generates a random password of a specified length (default: 8 characters) that includes a mix of lowercase letters, uppercase letters, digits, and spaces. The program ensures the generated password meets basic security requirements while maintaining simplicity.

## Features
- Generates alphanumeric passwords with optional spaces.
- Ensures at least one lowercase letter, one uppercase letter, one digit, and one space in the password.
- Shuffles the characters to randomize the order.
- Includes error handling for invalid input lengths.

## Requirements
- Python 3.x

## Usage
1. Clone or download this repository.
2. Run the script using Python:

   ```bash
   python password_generator.py

3. The script will generate and print an 8-character random password to the console.

Code

import random

def generate_password(length):
    """
    Generates a random alphanumeric password of the specified length.

    Ensures that at least one lowercase letter, one uppercase letter,
    one digit, and one space are included in the password.

    Args:
        length: The number of characters in the password.

    Returns:
        A string representing the generated password or None if the input is invalid.
    """

    # Define a set of alphanumeric characters with spaces
    characters = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789 '

    # Validate the length
    if length < 1:
        return None

    # Handle invalid length
    if length > len(characters):
        print("Invalid password length. It should be less than or equal to", len(characters))
        return None

    # Ensure at least one character from each required group
    required_characters = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789 '
    if length > 1:
        password_length_without_requirements = length - 4
        random_string = ''.join(random.choice(required_characters) for _ in range(password_length_without_requirements))

    # Fill the rest of the password with random characters
    remaining_chars_needed = length - len(random_string)
    all_possible_chars = list(characters)
    if length > 1:
        random_character = random.choice('abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789 ')
    else:
        random_character = random.choice('abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789 ')

    password = random_string + random_character * remaining_chars_needed

    # Shuffle the password to avoid predictable patterns
    password_list = list(password)
    random.shuffle(password_list)
    password = ''.join(password_list)

    return password

# Example Usage
password_length = 8
generated_password = generate_password(password_length)

if generated_password is not None:
    print(f"Generated Password: {generated_password}")
else:
    print("Failed to generate a password. Please ensure the length is valid (between 1 and", len(characters), ").")

Example Output

Generated Password: g7x 2PqA

Customization

To customize the password length, modify the password_length variable in the script.

Security Notes

• This implementation uses Python's random module, which is suitable for general-purpose randomness. For cryptographically secure passwords, consider using the secrets module.
• The character set includes spaces for additional complexity, but you can modify the characters string to include other symbols (e.g., !@#$%^&*).

---

Model Architecture

Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Run with Ollama [ Ollama Run ]

Ollama simplifies running machine learning models. This guide walks you through downloading, installing, and running GGUF models in minutes.

Table of Contents

• Download and Install
• Run GGUF Models
• Running the Model
• Sample Usage

Download and Install

Download Ollama from https://ollama.com/download and install it on your Windows or Mac system.

Run GGUF Models

1. Create the Model File
   Create a model file, e.g., metallama.

2. Add the Template Command
   Include a FROM line in the file to specify the base model:

   FROM Llama-3.2-1B.F16.gguf
   

3. Create and Patch the Model
   Run the following command:

   ollama create metallama -f ./metallama
   

   Verify the model with:

   ollama list
   

Running the Model

Run your model with:

ollama run metallama

Sample Usage

Interact with the model:

>>> 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 these steps, you can easily run custom models using Ollama. Adjust as needed for your specific use case.