Qwen3-0.6B-GGUF-for-24GB-VRAM

This 24GB VRAM-compatible, quantized model, designed for running with llama.cpp on 24GB VRAM, is a fully free software model published for maximum quality performance. You can find the Llama.cpp GitHub Repository at https://github.com/ggml-org/llama.cpp and learn more about GNU Free Software Philosophy at https://www.gnu.org/philosophy/free-sw.html. These resources provide the necessary information and tools to ensure that the models run with optimal quality on 24GB VRAM GPU setups.

Quick start on CPU and GPU

1. Install llama.cpp from https://github.com/ggml-org/llama.cpp

$ git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
# remove -D option below if you do not gave GPU
cmake -B build -DGGML_CUDA=ON;
cmake --build build --config Release;
cd build;
sudo make install;

2. Run it on CPU as:

llama-server --jinja -m Qwen3-0.6B-Q8_0.gguf

2. Or run it on GPU as:

llama-server --jinja -fa -c 8192 -ngl 999 -v --log-timestamps --host 192.168.1.68 -m Qwen3-0.6B-Q8_0.gguf

3. To disable thinking process, just add /no_think in the system message.

Qwen3-0.6B

Qwen3 Highlights

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:

• Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios.
• Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
• Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
• Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
• Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation.

Model Overview

Qwen3-0.6B has the following features:

• Type: Causal Language Models
• Training Stage: Pretraining & Post-training
• Number of Parameters: 0.6B
• Number of Paramaters (Non-Embedding): 0.44B
• Number of Layers: 28
• Number of Attention Heads (GQA): 16 for Q and 8 for KV
• Context Length: 32,768

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

If you encounter significant endless repetitions, please refer to the Best Practices section for optimal sampling parameters, and set the presence_penalty to 1.5.

Best Practices

To achieve optimal performance, we recommend the following settings:

1. Sampling Parameters:

   • For thinking mode (enable_thinking=True), use Temperature=0.6, TopP=0.95, TopK=20, and MinP=0. DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions.
   • For non-thinking mode (enable_thinking=False), we suggest using Temperature=0.7, TopP=0.8, TopK=20, and MinP=0.
   • For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.

2. Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.

3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.

   • Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
   • Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."

4. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.

Citation

If you find our work helpful, feel free to give Qwen a cite.

@misc{qwen3,
    title  = {Qwen3},
    url    = {https://qwenlm.github.io/blog/qwen3/},
    author = {Qwen Team},
    month  = {April},
    year   = {2025}
}