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--- |
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base_model: Qwen/Qwen3-30B-A3B |
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library_name: transformers |
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license: apache-2.0 |
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license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE |
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pipeline_tag: text-generation |
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tags: |
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- llama-cpp |
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- gguf-my-repo |
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--- |
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# Triangle104/Qwen3-30B-A3B-Q3_K_S-GGUF |
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This model was converted to GGUF format from [`Qwen/Qwen3-30B-A3B`](https://huggingface.co/Qwen/Qwen3-30B-A3B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. |
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Refer to the [original model card](https://huggingface.co/Qwen/Qwen3-30B-A3B) for more details on the model. |
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--- |
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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: |
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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. |
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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. |
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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. |
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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. |
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Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation. |
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Model Overview |
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- |
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Qwen3-30B-A3B has the following features: |
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Type: Causal Language Models |
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Training Stage: Pretraining & Post-training |
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Number of Parameters: 30.5B in total and 3.3B activated |
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Number of Paramaters (Non-Embedding): 29.9B |
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Number of Layers: 48 |
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Number of Attention Heads (GQA): 32 for Q and 4 for KV |
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Number of Experts: 128 |
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Number of Activated Experts: 8 |
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Context Length: 32,768 natively and 131,072 tokens with YaRN. |
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--- |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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llama-cli --hf-repo Triangle104/Qwen3-30B-A3B-Q3_K_S-GGUF --hf-file qwen3-30b-a3b-q3_k_s.gguf -p "The meaning to life and the universe is" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo Triangle104/Qwen3-30B-A3B-Q3_K_S-GGUF --hf-file qwen3-30b-a3b-q3_k_s.gguf -c 2048 |
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``` |
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./llama-cli --hf-repo Triangle104/Qwen3-30B-A3B-Q3_K_S-GGUF --hf-file qwen3-30b-a3b-q3_k_s.gguf -p "The meaning to life and the universe is" |
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``` |
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or |
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``` |
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./llama-server --hf-repo Triangle104/Qwen3-30B-A3B-Q3_K_S-GGUF --hf-file qwen3-30b-a3b-q3_k_s.gguf -c 2048 |
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``` |
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