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README.md
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This model was converted to GGUF format from [`Qwen/Qwen3-8B`](https://huggingface.co/Qwen/Qwen3-8B) 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/Qwen3-8B) for more details on the model.
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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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This model was converted to GGUF format from [`Qwen/Qwen3-8B`](https://huggingface.co/Qwen/Qwen3-8B) 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/Qwen3-8B) 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
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series, offering a comprehensive suite of dense and mixture-of-experts
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(MoE) models. Built upon extensive training, Qwen3 delivers
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groundbreaking advancements in reasoning, instruction-following, agent
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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,
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surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models
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(in non-thinking mode) on mathematics, code generation, and commonsense
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logical reasoning.
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Superior human preference alignment, excelling in
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creative writing, role-playing, multi-turn dialogues, and instruction
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following, to deliver a more natural, engaging, and immersive
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conversational experience.
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Expertise in agent capabilities, enabling precise
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integration with external tools in both thinking and unthinking modes
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and achieving leading performance among open-source models in complex
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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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Qwen3-8B 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: 8.2B
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Number of Paramaters (Non-Embedding): 6.95B
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Number of Layers: 36
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Number of Attention Heads (GQA): 32 for Q and 8 for KV
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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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