205 lines
8.2 KiB
Markdown
205 lines
8.2 KiB
Markdown
---
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license: creativeml-openrail-m
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pipeline_tag: text-generation
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library_name: transformers
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language:
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- en
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base_model:
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- meta-llama/Llama-3.2-3B-Instruct
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tags:
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- codepy
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- safetensors
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- ollama
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- llama-cpp
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- trl
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- deep-think
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- coder
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---
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# **Codepy 3B Deep Think Model File**
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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.
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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.
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| **Model Content** | **Size** | **Description** | **Upload Status** |
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|-----------------------------------|----------------|------------------------------------------------|-------------------|
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| `.gitattributes` | 1.57 kB | Git LFS configuration for large files. | Uploaded |
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| `README.md` | 221 Bytes | Basic repository information. | Updated |
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| `config.json` | 994 Bytes | Model configuration with architectural details. | Uploaded |
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| `generation_config.json` | 248 Bytes | Default generation parameters. | Uploaded |
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| `pytorch_model-00001-of-00002.bin`| 4.97 GB | Sharded PyTorch model weights (part 1 of 2). | Uploaded (LFS) |
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| `pytorch_model-00002-of-00002.bin`| 1.46 GB | Sharded PyTorch model weights (part 2 of 2). | Uploaded (LFS) |
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| `pytorch_model.bin.index.json` | 21.2 kB | Index file mapping model shards. | Uploaded |
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| `special_tokens_map.json` | 477 Bytes | Maps special tokens to their respective IDs. | Uploaded |
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| `tokenizer.json` | 17.2 MB | Full tokenizer vocabulary and merges. | Uploaded (LFS) |
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| `tokenizer_config.json` | 57.5 kB | Tokenizer configuration details. | Uploaded |
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| Run with LM Studio | Details |
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|--------------------------|-----------------------------------------------------------------------------------------------|
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| *Run with LM Studio* | https://lmstudio.ai/ |
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| *Demo on LM Studio* | https://drive.google.com/file/d/1CHdfjYrwMnk9ACvS40Abfy3xNXnCubKG/view?usp=sharing |
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| *Codepy-Deepthink-3B-GGUF* | https://huggingface.co/prithivMLmods/Codepy-Deepthink-3B-GGUF |
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---
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# **<span style="color:red;">Sample Deepthink Inference</span>**
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>>> Develop a Python program to generate random passwords that consist of 8 characters. Not applicable
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```markdown
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# Random Password Generator
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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.
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## Features
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- Generates alphanumeric passwords with optional spaces.
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- Ensures at least one lowercase letter, one uppercase letter, one digit, and one space in the password.
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- Shuffles the characters to randomize the order.
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- Includes error handling for invalid input lengths.
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## Requirements
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- Python 3.x
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## Usage
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1. Clone or download this repository.
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2. Run the script using Python:
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```bash
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python password_generator.py
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```
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3. The script will generate and print an 8-character random password to the console.
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## Code
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```python
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import random
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def generate_password(length):
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"""
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Generates a random alphanumeric password of the specified length.
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Ensures that at least one lowercase letter, one uppercase letter,
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one digit, and one space are included in the password.
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Args:
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length: The number of characters in the password.
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Returns:
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A string representing the generated password or None if the input is invalid.
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"""
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# Define a set of alphanumeric characters with spaces
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characters = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789 '
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# Validate the length
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if length < 1:
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return None
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# Handle invalid length
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if length > len(characters):
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print("Invalid password length. It should be less than or equal to", len(characters))
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return None
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# Ensure at least one character from each required group
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required_characters = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789 '
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if length > 1:
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password_length_without_requirements = length - 4
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random_string = ''.join(random.choice(required_characters) for _ in range(password_length_without_requirements))
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# Fill the rest of the password with random characters
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remaining_chars_needed = length - len(random_string)
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all_possible_chars = list(characters)
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if length > 1:
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random_character = random.choice('abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789 ')
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else:
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random_character = random.choice('abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789 ')
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password = random_string + random_character * remaining_chars_needed
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# Shuffle the password to avoid predictable patterns
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password_list = list(password)
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random.shuffle(password_list)
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password = ''.join(password_list)
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return password
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# Example Usage
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password_length = 8
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generated_password = generate_password(password_length)
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if generated_password is not None:
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print(f"Generated Password: {generated_password}")
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else:
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print("Failed to generate a password. Please ensure the length is valid (between 1 and", len(characters), ").")
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```
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## Example Output
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```
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Generated Password: g7x 2PqA
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```
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## Customization
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To customize the password length, modify the `password_length` variable in the script.
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## Security Notes
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- This implementation uses Python's `random` module, which is suitable for general-purpose randomness. For cryptographically secure passwords, consider using the `secrets` module.
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- The character set includes spaces for additional complexity, but you can modify the `characters` string to include other symbols (e.g., `!@#$%^&*`).
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---
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# **Model Architecture**
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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.
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# **Run with Ollama [ Ollama Run ]**
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Ollama simplifies running machine learning models. This guide walks you through downloading, installing, and running GGUF models in minutes.
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## Table of Contents
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- [Download and Install](#download-and-install)
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- [Run GGUF Models](#run-gguf-models)
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- [Running the Model](#running-the-model)
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- [Sample Usage](#sample-usage)
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## Download and Install
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Download Ollama from [https://ollama.com/download](https://ollama.com/download) and install it on your Windows or Mac system.
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## Run GGUF Models
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1. **Create the Model File**
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Create a model file, e.g., `metallama`.
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2. **Add the Template Command**
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Include a `FROM` line in the file to specify the base model:
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```bash
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FROM Llama-3.2-1B.F16.gguf
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```
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3. **Create and Patch the Model**
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Run the following command:
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```bash
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ollama create metallama -f ./metallama
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```
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Verify the model with:
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```bash
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ollama list
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```
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## Running the Model
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Run your model with:
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```bash
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ollama run metallama
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```
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### Sample Usage
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Interact with the model:
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```plaintext
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>>> write a mini passage about space x
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Space X, the private aerospace company founded by Elon Musk, is revolutionizing the field of space exploration...
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```
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---
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With these steps, you can easily run custom models using Ollama. Adjust as needed for your specific use case. |