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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
qwen3.8-27b-imatrix-nvfp4-mtp
Qwen3.8-27B in cdiamond's iMatrix-calibrated mixed-precision NVFP4
build. Selected attention and feed-forward tensors retain higher
precision. Includes the F16 vision projector and enables the embedded
MTP draft head with one draft token per step.
Repository: localaiLicense: apache-2.0
underdog-saluki-27b-1.0-abliterated
Kujira's abliterated adaptation of Underdog Saluki 27B 1.0 uses mixed
IQ2 weights with an embedded MTP head. This text-only llama.cpp build
uses ordinary decoding; the MTP variant enables speculative decoding.
Repository: localaiLicense: apache-2.0
swift-qwen3.8-27b
Swift-Qwen3.8-27B is UkisAI's reasoning-efficient fine-tune of Qwen3.8-27B.
The publisher reports 58.3% fewer thinking tokens with less than 1% quality loss.
This Q4_K_M GGUF includes the F16 vision projector and enables MTP speculative decoding.
The weights use the Swift Open License v1.0.
Repository: localaiLicense: swift-open-license-1.0
qwopus3.8-27b-flash-v2
Qwopus3.8-27B-Flash-V2 is a new post-training release for reasoning and agent
workloads. This Q4_K_M GGUF includes the F32 vision projector and uses
llama.cpp's embedded chat template with MTP speculative decoding.
Repository: localaiLicense: apache-2.0
qwopus3.8-27b-flash-v2-q8
Qwopus3.8-27B-Flash-V2 is a new post-training release for reasoning and agent
workloads. This Q8_0 GGUF includes the F32 vision projector and uses
llama.cpp's embedded chat template with MTP speculative decoding.
Repository: localaiLicense: apache-2.0
qwopus3.8-27b-flash
Qwopus3.8-27B-Flash is a Qwen3.8-27B fine-tune for reasoning and agent
workloads. This Q4_K_M GGUF includes the F32 vision projector and uses
llama.cpp's embedded chat template with MTP speculative decoding.
The publisher reports a known Python code indentation issue.
Repository: localaiLicense: apache-2.0
qwopus3.8-27b-flash-q8
Qwopus3.8-27B-Flash is a Qwen3.8-27B fine-tune for reasoning and agent
workloads. This Q8_0 GGUF includes the F32 vision projector and uses
llama.cpp's embedded chat template with MTP speculative decoding.
The publisher reports a known Python code indentation issue.
Repository: localaiLicense: apache-2.0
apodex-1.1-mini-q4-mtp
Apodex-1.1-mini with MTP speculative decoding enabled on the recommended
Q4_K_M GGUF. The model carries its native MTP head, so it needs no separate
draft model. The F16 vision projector supports multimodal prompts.
Repository: localaiLicense: apache-2.0
glm-5.3-flash-q4
GLM-5.3-Flash is Z.ai's natively multimodal 320B-parameter mixture-of-experts
model with 18B active parameters. It combines sparse and linear attention
for coding, agentic work, tool use, vision, and long-context tasks. This
entry uses the UD-Q4_K_XL GGUF quantization and enables the model's MTP
speculative-decoding head.
Repository: localaiLicense: mit
glm-5.3-flash-q8
GLM-5.3-Flash is Z.ai's natively multimodal 320B-parameter mixture-of-experts
model with 18B active parameters. It combines sparse and linear attention
for coding, agentic work, tool use, vision, and long-context tasks. This
entry uses the higher-quality Q8_0 GGUF quantization and enables the model's
MTP speculative-decoding head.
Repository: localaiLicense: mit
dirk-qwen3.8-27b-q4
Dirk is a Qwen3.8 27B vision-language model with a concise chat template
for agentic coding, reasoning, tool use, and general knowledge tasks. It
preserves the model's MTP head for speculative decoding and supports a
262K-token context window.
This default entry uses the Q4_K_XL GGUF and F16 vision projector. A
choice of Q5_K_XL, Q6_K_XL, and Q8_K_XL builds is available through variants.
Repository: localaiLicense: apache-2.0
dirk-qwen3.8-27b-q8
Dirk in the higher-quality Q8_K_XL GGUF format, with MTP speculative
decoding and the shared F16 vision projector for multimodal prompts.
Repository: localaiLicense: apache-2.0
dirk-qwen3.8-27b-q5
Dirk in the higher-quality Q5_K_XL GGUF format, with MTP speculative
decoding and the shared F16 vision projector for multimodal prompts.
Repository: localaiLicense: apache-2.0
dirk-qwen3.8-27b-q6
Dirk in the higher-quality Q6_K_XL GGUF format, with MTP speculative
decoding and the shared F16 vision projector for multimodal prompts.
Repository: localaiLicense: apache-2.0
qwen3.8-27b-uncensored-q4
Qwen3.8-27B-Uncensored reduces refusal behavior while retaining the base
model's text, vision, reasoning, and tool-use capabilities. Its integrated
MTP head supports speculative decoding without a separate draft model.
This default entry uses the Q4_K_M GGUF and F16 vision projector. A
higher-quality Q8_0 model is available as a variant.
Repository: localaiLicense: apache-2.0
huihui-qwen3.8-27b-abliterated
Huihui Qwen3.8 27B is an abliterated vision-language model published by
huihui-ai. This BF16 GGUF build includes the shared BF16 vision projector
and enables MTP speculative decoding through llama.cpp. Q4_K, Q5_K, Q6_K,
and Q8_0 variants are available as smaller downloads.
Repository: localaiLicense: apache-2.0
huihui-qwen3.8-27b-abliterated-q4
Huihui Qwen3.8 27B in Q4_K GGUF format, with the shared BF16 vision
projector and MTP speculative decoding through llama.cpp.
Repository: localaiLicense: apache-2.0
huihui-qwen3.8-27b-abliterated-q5
Huihui Qwen3.8 27B in Q5_K GGUF format, with the shared BF16 vision
projector and MTP speculative decoding through llama.cpp.
Repository: localaiLicense: apache-2.0
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