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---
license: creativeml-openrail-m
pipeline_tag: text-generation
library_name: transformers
language:
- en
base_model:
- meta-llama/Llama-3.2-3B-Instruct
tags:
- codepy
- safetensors
- ollama
- llama-cpp
- trl
- deep-think
- coder
---
# **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 |
---
# **<span style="color:red;">Sample Deepthink Inference</span>**
>>> Develop a Python program to generate random passwords that consist of 8 characters. Not applicable
```markdown
# 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
```python
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](#download-and-install)
- [Run GGUF Models](#run-gguf-models)
- [Running the Model](#running-the-model)
- [Sample Usage](#sample-usage)
## Download and Install
Download Ollama from [https://ollama.com/download](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:
```bash
FROM Llama-3.2-1B.F16.gguf
```
3. **Create and Patch the Model**
Run the following command:
```bash
ollama create metallama -f ./metallama
```
Verify the model with:
```bash
ollama list
```
## Running the Model
Run your model with:
```bash
ollama run metallama
```
### Sample Usage
Interact with the model:
```plaintext
>>> 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.

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