Codepy-Deepthink-3B-GGUF
| Property | Value |
|---|---|
| Base Model | meta-llama/Llama-3.2-3B-Instruct |
| Model Type | Fine-tuned Language Model |
| Available Formats | F16, Q4_K_M, Q5_K_M, Q8_0 GGUF |
| Model URL | https://huggingface.co/prithivMLmods/Codepy-Deepthink-3B-GGUF |
What is Codepy-Deepthink-3B-GGUF?
Codepy-Deepthink-3B-GGUF is a specialized fine-tuned variant of the LLaMA 3.2 architecture, specifically optimized for tasks requiring deep reasoning, logical structuring, and problem-solving capabilities. Built upon the meta-llama/Llama-3.2-3B-Instruct base model, it offers enhanced performance for programming, education, and creative writing applications.
Implementation Details
The model is available in multiple GGUF quantization formats, including full precision F16 (6.43GB), Q4_K_M (2.02GB), Q5_K_M (2.32GB), and Q8_0 (3.42GB), allowing users to balance performance and resource requirements. It implements an auto-regressive transformer architecture with optimizations from the LLaMA 3.2 framework, incorporating both supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF).
- Multiple quantization options for different deployment scenarios
- Compatible with popular inference platforms like LM Studio and Ollama
- Optimized for both structured and unstructured data processing
- Enhanced context understanding and logical reasoning capabilities
Core Capabilities
- Deep reasoning and logical analysis
- Step-by-step solution generation
- Programming and code generation
- Creative content creation
- Educational content development
Frequently Asked Questions
Q: What makes this model unique?
The model's specialized fine-tuning for deep reasoning and programming tasks, combined with its various quantization options, makes it particularly suitable for applications requiring detailed logical analysis and step-by-step solution generation.
Q: What are the recommended use cases?
The model excels in programming tasks, educational content creation, problem-solving scenarios, and applications requiring structured logical thinking. It's particularly effective for generating step-by-step solutions and detailed explanations.
