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qwen3.6-35b-a3b-ud-q4_k_xl-strata

Qwen3.6-35B-A3B, a 35B-parameter mixture-of-experts model with 3B active parameters, in Unsloth's 4-bit (Unsloth Dynamic), the smallest GGUF with the MTP layer (about 24 GB to download). It reads images through Unsloth's vision projector, and the Strata backend parses its reasoning and tool calls. Needs an NVIDIA or AMD GPU (the Intel Arc and Apple builds of Strata do not run this model) and about 32 GB of RAM: the engine keeps every expert it can in VRAM and computes the rest on the CPU. Runs only on the experimental strata backend. On the first load the strata backend exports the tokenizer and chat template from the GGUF file (seconds); the engine reads the GGUF as it is. The MTP draft layer inside the file is used for speculative decoding. Licensed under the Apache License 2.0 (Qwen/Qwen3.6-35B-A3B). The GGUF files are the Unsloth quantization; Strata is by Niko1221.

Repository: localaiLicense: apache-2.0

qwen3.6-35b-a3b-ud-q6_k_xl-strata

Qwen3.6-35B-A3B, a 35B-parameter mixture-of-experts model with 3B active parameters, in Unsloth's 6-bit (Unsloth Dynamic), closer to the full model GGUF with the MTP layer (about 34 GB to download). It reads images through Unsloth's vision projector, and the Strata backend parses its reasoning and tool calls. Needs an NVIDIA or AMD GPU (the Intel Arc and Apple builds of Strata do not run this model) and about 44 GB of RAM: the engine keeps every expert it can in VRAM and computes the rest on the CPU. Runs only on the experimental strata backend. On the first load the strata backend exports the tokenizer and chat template from the GGUF file (seconds); the engine reads the GGUF as it is. The MTP draft layer inside the file is used for speculative decoding. Licensed under the Apache License 2.0 (Qwen/Qwen3.6-35B-A3B). The GGUF files are the Unsloth quantization; Strata is by Niko1221.

Repository: localaiLicense: apache-2.0

qwen3.6-35b-a3b-q8_0-strata

Qwen3.6-35B-A3B, a 35B-parameter mixture-of-experts model with 3B active parameters, in Unsloth's 8-bit, the closest to the full model GGUF with the MTP layer (about 39 GB to download). It reads images through Unsloth's vision projector, and the Strata backend parses its reasoning and tool calls. Needs an NVIDIA or AMD GPU (the Intel Arc and Apple builds of Strata do not run this model) and about 52 GB of RAM: the engine keeps every expert it can in VRAM and computes the rest on the CPU. Runs only on the experimental strata backend. On the first load the strata backend exports the tokenizer and chat template from the GGUF file (seconds); the engine reads the GGUF as it is. The MTP draft layer inside the file is used for speculative decoding. Licensed under the Apache License 2.0 (Qwen/Qwen3.6-35B-A3B). The GGUF files are the Unsloth quantization; Strata is by Niko1221.

Repository: localaiLicense: apache-2.0

atomic-minicpm-v-4.7-35b-a3b-uncensored-q4

AtomicChat's abliterated MiniCPM-V 4.7 is a 35B mixture-of-experts model with 3B active parameters for chat and image understanding. This AD-Q4_K-IQ4_XS build includes all 11 weight shards and the F16 vision projector, with the embedded chat template and a 32K-token default context.

Repository: localaiLicense: apache-2.0

atomic-minicpm-v-4.7-35b-a3b-uncensored-q6

AtomicChat's abliterated MiniCPM-V 4.7 is a 35B mixture-of-experts model with 3B active parameters for chat and image understanding. This AD-Q6_K build includes all 16 weight shards and the F16 vision projector, with the embedded chat template and a 32K-token default context.

Repository: localaiLicense: apache-2.0

ornith-1.5-35b-a3b-apex

Ornith-1.5-35B-A3B is an MIT-licensed Qwen3.5 mixture-of-experts model from Ornith AI for agentic coding, reasoning, repository-level software tasks, and tool use. It supports text and image input with a context window of 262K tokens. This default entry uses the APEX Balanced GGUF and BF16 vision projector. Compact APEX and MTP-enabled APEX builds are available as variants.