Varadrajan/ITDR-SFT-Qwen2.5-3B-v1
Varadrajan/ITDR-SFT-Qwen2.5-3B-v1 is a 3.1 billion parameter instruction-tuned causal language model based on the Qwen2.5 architecture. This model has been fine-tuned and converted to the GGUF format, making it suitable for efficient local deployment and inference. It is specifically optimized for use with llama.cpp and Ollama, providing readily available model files for various quantization levels. Its primary differentiator is its fine-tuning process, which leveraged Unsloth for accelerated training, and its direct availability in GGUF for easy integration into local inference setups.
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Overview
Varadrajan/ITDR-SFT-Qwen2.5-3B-v1 is an instruction-tuned language model built upon the Qwen2.5-3B architecture. This model has undergone a fine-tuning process and is provided in the GGUF format, which is optimized for efficient local execution, particularly with tools like llama.cpp and Ollama.
Key Characteristics
- Base Model: Qwen2.5-3B-Instruct, a 3.1 billion parameter causal language model.
- Format: Converted to GGUF, enabling broad compatibility with local inference engines.
- Quantization: Available in multiple quantization levels, including
Q8_0.ggufandQ4_K_M.gguf, to balance performance and resource usage. - Training Efficiency: Fine-tuned using Unsloth, which facilitated a 2x faster training process.
- Deployment: Includes an Ollama Modelfile for simplified deployment and integration.
Good For
- Local Inference: Ideal for developers and users seeking to run a capable instruction-tuned model on consumer-grade hardware.
- llama.cpp Users: Directly compatible with
llama.cppfor both text-only and multimodal (if applicable to the base Qwen2.5 model) applications. - Ollama Integration: Streamlined setup for Ollama environments, allowing quick experimentation and deployment.
- Resource-Constrained Environments: The GGUF format and various quantization options make it suitable for systems with limited memory or computational power.