DeepSeek-R1-Distill-Qwen-1.5B-GGUF
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DeepSeek-R1-Distill-Qwen-32B-GGUF
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View details ↗gemma-4-12B-GGUF
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View details ↗GLM-4.7-Flash-GGUF
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View details ↗Kimi-Linear-48B-A3B-Instruct-GGUF
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View details ↗LFM2.5-8B-A1B-GGUF
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View details ↗NVIDIA-Nemotron-3-Nano-30B-A3B-BF16-GGUF
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View details ↗phi-2-GGUF
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View details ↗Qwen-AgentWorld-35B-A3B-GGUF
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View details ↗Qwen2.5-0.5B-Instruct-GGUF
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View details ↗Qwen2.5-1.5B-Instruct-GGUF
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View details ↗Qwen2.5-7B-Instruct-GGUF
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View details ↗Qwen2.5-Coder-32B-Instruct-GGUF
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View details ↗Qwen2.5-Coder-7B-Instruct-GGUF
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View details ↗Qwen2.5-VL-3B-Instruct-GGUF
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View details ↗Qwen2.5-VL-7B-Instruct-GGUF
DhruvalLabs> [!IMPORTANT] > **This VLM requires TWO files** — a text backbone GGUF and the `mmproj` vision encoder GGUF. > Download one text backbone (e.g. Q4_K_M) **and** the mmproj file. Both must be in the same folder. 1. Search for `Dhptl/Qwen2.5-VL-7B-Instruct` in LM Studio 2. Download the Q4_K_M text file **and** the mmproj file 3. Load the model — LM Studio automatically uses both files > **Why is mmproj always F16?** > The vision encoder maps image pixels to token embeddings. Quantizing it causes > visible visual artifacts and degraded image understanding. It stays at F16 (half precision) > which is already very efficient at ~1-2GB for most models.
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View details ↗Qwen3-0.6B-GGUF
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View details ↗Qwen3-32B-GGUF
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View details ↗Qwen3-4B-Instruct-2507-GGUF
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View details ↗Qwen3-4B-Thinking-2507-GGUF
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View details ↗Qwen3.8-27B-GGUF
DhruvalLabs> [!IMPORTANT] > **This VLM requires TWO files** — a text backbone GGUF and the `mmproj` vision encoder GGUF. > Download one text backbone (e.g. Q4_K_M) **and** the mmproj file. Both must be in the same folder. 1. Search for `DhruvalLabs/Qwen3.8-27B` in LM Studio 2. Download the Q4_K_M text file **and** the mmproj file 3. Load the model — LM Studio automatically uses both files > **Why is mmproj always F16?** > The vision encoder maps image pixels to token embeddings. Quantizing it causes > visible visual artifacts and degraded image understanding. It stays at F16 (half precision) > which is already very efficient at ~1-2GB for most models.
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View details ↗qwen3-8b-claude-agentic-fable5-GGUF
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View details ↗Qwen3-8B-GGUF
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View details ↗Qwen3-Coder-30B-A3B-Instruct-GGUF
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