20 models
Qwen3 Coder 30B A3B Unsloth Dynamic 2.0 GGUF MoE
Qwen3 Coder 30B A3B 1M Unsloth Dynamic 2.0 GGUF MoE
Qwen3-Coder-30B-A3B-Instruct-GGUF from: https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF
Unsloth Dynamic 2.0 Quants achieves 1M tokens & superior accuracy & SOTA quantization performance. Select UD-IQ3_XXS for 16GB VRAM, UD-Q4_K_XL for 24GB VRAM, or UD-Q5_K_XL/UD-Q6_K_XL for 32GB VRAM.
Qwen3-Coder is available in multiple sizes. Today, we’re excited to introduce Qwen3-Coder-30B-A3B-Instruct. This streamlined model maintains impressive performance and efficiency ........
Unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF with defaults to consume roughly 32 GB of VRAM
Qwen3-Coder is a highly capable coding assistant, based on Qwen3-Coder-30B-A3B-Instruct. She is designed for local development and agentic coding workflows
Alibaba's Qwen3-Coder-30B 4bit with 256k token context. Enhanced tool calling for agentic coding tasks
Alibaba's Qwen3-Coder-30B 4bit with 1M token context. Enhanced tool calling for agentic coding tasks
model Qwen3-Coder-30B-A3B-Instruct-Q5_K_M
Quantized Q4_K_S version of Qwen3-coder 30B FP16 model
From qwen3-coder:30b . Compatible with opencode (both).
https://huggingface.co/burtenshaw/Qwen3-30B-A3B-python-coder
qwen3-30B-coder-tools.Q4_0
Four-way omnimerge_v2 over Qwen3.5-9B (DeltaCoder 0.55 / Qwopus 0.30 / Ornith 0.15), built for multi-turn agentic coding: it keeps a task moving across turns and tool calls instead of re-issuing the same call. 9B dense, MTP head
Qwen 3.8 27B, Distilled against Opus 5.5, GPT 6 Astra, and Grok 4.7
Quantized version of Qwen3 models (4B,8B,14B,32B, 30B-moe) optimized for tool usage in Cline / Roo Code and solving Complex Problems.