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Qwen3.1 · Ollama
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  • dedeprogames/open-reasoner

    OpenReasoner is a fine-tuned qwen3:8b and qwen3:1.7b model trained on the OpenThoughts-114k dataset.

    tools thinking 1.7b 8b

    184  Pulls 2  Tags Updated  6 months ago

  • s44t12/big

    Renamed qwen3:14b

    tools thinking

    173  Pulls 1  Tag Updated  11 months ago

  • s44t12/mini

    Renamed qwen3:1.7b.

    tools thinking

    58  Pulls 1  Tag Updated  11 months ago

  • Scoutty/qwen3.16k

    tools thinking

    18  Pulls 1  Tag Updated  7 months ago

  • orcarouter/Qwen3.8-Flash-Next-Uncensored

    Qwen3.8-Flash-Next tensor-level abliterated— a latest Qwen4-architecture MoE (~177B total / ~6B active) with vision, reasoning, tool-calling, and 262K context. MLX 4/6/8-bit for Apple Silicon. Research use only.

    vision tools thinking

    1,990  Pulls 4  Tags Updated  4 days ago

  • smtek/Qwen3.8-27B

    Quants from Q2 up to Q5 from Unsloth K_M and UD_K_XL and builds for 12, 16 and 24 GB

    vision

    13.1K  Pulls 22  Tags Updated  1 week ago

  • Whittle/Qwen3.8-Whittle-MoE-27B-A17.8B

    The architecture is a partition, not a rebuild. All 64 layers keep the parent's attention side untouched: the 3 to 1 hybrid of gated deltanet layers and full attention, 16 attention layers in all, hidden size 5120. The surgery is in the feed forward. Each

    147  Pulls 4  Tags Updated  1 week ago

  • jstzwhc/Qwen3-TTS

    Qwen3-TTS-12Hz-1.7B-Q4_K_M

    158  Pulls 1  Tag Updated  1 week ago

  • dna5rm/qwen3.8

    Qwen3.8-27B Q4_K_M, default context 131072, tuned for a 32 GB NVIDIA RTX 5090.

    vision tools thinking

    54  Pulls 1  Tag Updated  1 week ago

  • carstenuhlig/omnicoder-9b

    9B coding agent based on Qwen3.5-9B, fine-tuned on 425K real agentic traces from Claude Opus 4.6, GPT-5.4, and Gemini 3.1. Reads before it writes, traces bugs to the root cause, doesn't clobber your existing code.

    tools thinking

    16.2K  Pulls 3  Tags Updated  5 months ago

  • kishor4shinde/infinitech

    Infinitech Short description A powerful local AI assistant built on Qwen3 14B for reasoning, coding, problem-solving, and clear technical communication. Full description Infinitech is a thoughtful and capable local AI assistant powered by Qwen3 14B. It is

    tools thinking

    51  Pulls 1  Tag Updated  3 weeks ago

  • VladimirGav/Qwen3.6-27B-16GB-VRAM-Uncensored

    7,498  Pulls 1  Tag Updated  4 months ago

  • satgeze/qwen36-35b-uncensored-1m

    Qwen3.6-35B uncensored flagship: 1M certified 70/70, vision, MTP grafted. One file, four capabilities.

    vision tools thinking

    3,302  Pulls 2  Tags Updated  1 month ago

  • kwangsuklee/Qwen3.5-9B.Q4_K_M-Claude-4.6-Opus-Reasoning-Distilled-v2

    26.3.18. Ver.2 update: This iteration is powered by 14,000+ premium Claude 4.6 Opus-style general reasoning samples, with a major focus on achieving massive gains in reasoning efficiency while actively improving peak accuracy.

    7,735  Pulls 1  Tag Updated  5 months ago

  • SetneufPT/Qwen3.6-27B-MTP_Q3_32K_16GB-GPU

    Custom model for coding with agents to use locally with 16gb GPUs (working fine...)

    1,897  Pulls 1  Tag Updated  2 months ago

  • SetneufPT/Qwen3.5-9B-Coder_Q4_256k_ABL_16GB-GPU

    Custom model for coding with agents to use locally with 16gb GPUs (working very fine...)

    vision tools thinking

    1,271  Pulls 1  Tag Updated  1 month ago

  • kwangsuklee/Qwen3.5-4B.Q4_K_M-Claude-4.6-Opus-Reasoning-Distilled-v2

    26.3.18. Ver.2 update: This iteration is powered by 14,000+ premium Claude 4.6 Opus-style general reasoning samples, with a major focus on achieving massive gains in reasoning efficiency while actively improving peak accuracy.

    2,380  Pulls 1  Tag Updated  5 months ago

  • SetneufPT/Qwen3.6-27B-CODER-MTP_Q4_105k_24GB-GPU

    Custom model for coding with agents to use locally with 24gb GPUs - BEST FOR OPENCODE!

    vision tools thinking

    1,007  Pulls 1  Tag Updated  1 month ago

  • oamazonasgabriel/qwen3.5-9b

    A coding-optimized configuration of Qwen3.5-9B designed for 16 GB single-GPU hardware. The model uses the official Q4_K_M quantization (~6.6 GB weights), leaving ~9 GB headroom for KV cache — enabling 32K+ context windows comfortably.

    vision tools thinking

    954  Pulls 1  Tag Updated  2 months ago

  • reecdev/tiny3.5

    An attempt to compress Qwen3.5 into 500M and 1.5B parameters.

    tools thinking 500m 1.5b

    803  Pulls 2  Tags Updated  5 months ago

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