jinnam12/qwen-3b-brain-v3

TEXT GENERATIONConcurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 7, 2026Architecture:Transformer Featherless Exclusive Cold

jinnam12/qwen-3b-brain-v3 is a 3.1 billion parameter Qwen2.5-based instruction-tuned language model, fine-tuned and converted to GGUF format by jinnam12 using Unsloth. This model is optimized for efficient deployment and usage, particularly with `llama-cli` and Ollama, offering a compact yet capable solution for various language generation tasks.

Loading preview...

Overview

jinnam12/qwen-3b-brain-v3 is a 3.1 billion parameter language model based on the Qwen2.5 architecture, specifically instruction-tuned for enhanced performance. This model has been meticulously fine-tuned and converted into the GGUF format, leveraging the Unsloth framework for accelerated training and conversion.

Key Capabilities & Features

  • Efficient GGUF Format: Provided in the qwen2.5-3b-instruct.Q4_K_M.gguf file, ensuring broad compatibility with various inference engines.
  • Unsloth Optimization: Benefits from Unsloth's 2x faster training, making it an efficient choice for deployment.
  • Ollama Integration: Includes a pre-configured Ollama Modelfile for straightforward setup and use.
  • Command-Line Interface Support: Designed for easy interaction via llama-cli for text-only applications and llama-mtmd-cli for potential multimodal use cases, utilizing a Jinja templating system.

Use Cases

This model is well-suited for developers seeking a compact and efficient instruction-tuned LLM for local deployment. Its GGUF format and Ollama support make it ideal for applications requiring fast inference on consumer hardware, such as chatbots, content generation, and code assistance, where a 3.1B parameter model provides a good balance of performance and resource efficiency.

Finetunes
1 models