Qwen2.5-0.5B-200K-GGUF
| Property | Value |
|---|---|
| Parameter Count | 494M parameters |
| License | CreativeML OpenRAIL-M |
| Base Model | Qwen/Qwen2.5-0.5B |
| Training Dataset | HuggingFaceH4/ultrachat_200k |
| Language | English |
What is Qwen2.5-0.5B-200K-GGUF?
Qwen2.5-0.5B-200K-GGUF is a lightweight language model optimized for efficient inference using the GGUF format. Built on the Qwen2.5 architecture, this model has been fine-tuned on the UltraChat 200K dataset to enhance its conversational capabilities while maintaining a compact size of 494M parameters.
Implementation Details
The model comes in multiple quantization variants to suit different deployment needs: F16 (994MB), Q4_K_M (398MB), Q5_K_M (420MB), and Q8_0 (531MB). It's specifically designed for integration with Llama.cpp and Ollama, making it accessible for both local deployment and inference endpoints.
- Full precision F16 version for maximum accuracy
- Memory-optimized Q4_K_M variant for efficient deployment
- Balanced Q5_K_M version offering accuracy/size trade-off
- Q8_0 variant for moderate compression while maintaining quality
Core Capabilities
- Text generation and conversational AI tasks
- Efficient local deployment through Ollama integration
- Multiple quantization options for different use cases
- Optimized for English language processing
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient balance between size and capability, offering multiple quantization options while maintaining good performance through the UltraChat 200K training data.
Q: What are the recommended use cases?
The model is ideal for conversational AI applications, text generation tasks, and scenarios requiring local deployment with limited computational resources. It's particularly suited for integration with Ollama and Llama.cpp environments.
