Franz-Lynchtek/ginette-qwen3-4b-v1
The Franz-Lynchtek/ginette-qwen3-4b-v1 is a 4 billion parameter Qwen3-based language model, fine-tuned and converted to GGUF format. This model leverages Unsloth for accelerated training and efficient deployment. It is primarily designed for text-based applications and can be easily integrated into local inference setups using tools like llama-cli or Ollama.
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Model Overview
The ginette-qwen3-4b-v1 is a 4 billion parameter language model based on the Qwen3 architecture. It has been specifically fine-tuned and converted into the GGUF format, making it suitable for efficient local deployment and inference.
Key Characteristics
- Architecture: Based on the Qwen3 model family.
- Parameter Count: 4 billion parameters, offering a balance between performance and resource efficiency.
- Format: Provided in GGUF format, which is optimized for CPU and GPU inference with tools like
llama.cpp. - Training Efficiency: The model was fine-tuned using Unsloth, which enabled a 2x faster training process.
Deployment and Usage
This model is designed for straightforward integration into local inference environments. An Ollama Modelfile is included, simplifying deployment for users of the Ollama platform. For direct use, it supports llama-cli for text-only applications and llama-mtmd-cli for potential multimodal extensions, utilizing a Jinja-based prompt format.
Available Files
The primary model file available is qwen3-4b-instruct-2507.Q4_K_M.gguf, indicating a quantized version for optimized performance.