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qwen3.8-flash-next-unsloth-q8_0
Qwen3.8-Flash-Next, a 125B-parameter hybrid mixture-of-experts model, in
Unsloth's Dynamic quantization (Q8_0, 8-bit, the reference quality) (about 189
GB to download).
It reads images through the vision projector published for the original model,
and the Strata backend parses its reasoning and tool calls.
Needs about 136 GB of RAM to keep every expert in memory; with less, the
mapped experts are read from the SSD while it answers, which works and is much
slower. Keep the files on an SSD. Needs a GPU the strata backend supports;
NVIDIA with 12 GB of VRAM or more is the class Strata tests. Runs only on the
experimental strata backend.
On the first load the strata backend builds the Strata pack from the GGUF
files (a few minutes, a few GB of extra disk); a finished pack is never
rebuilt. The experts are read from the GGUF files in place (mapped); add
--resident-experts to engine_args to copy them into RAM when there is room.
Licensed under the Qwen Community License 1.0 (inherited from
Qwen/Qwen3.8-Flash-Next): free use, but a Model as a Service or AI Work
Assistant business needs a licence from Qwen. Read the LICENSE file in the
repository first.
The GGUF files are the Unsloth quantization; the vision projector is
ISTA-DASLab's file for the original model; Strata is by Niko1221. The MTP
draft layer is not configured here.
Repository: localaiLicense: qwen-community-1.0
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
qwen3.5-4b-3d-coder-q8
CCORE's early preview fine-tune of Qwen3.5-4B for coding and Three.js
scene generation. This text-only Q8_0 build pins a preview
checkpoint and uses the embedded chat template. MTP is not enabled.
Repository: localaiLicense: apache-2.0
sage-a1.5-q8
Sage-A1.5 is a 35B mixture-of-experts model based on Qwen3.6 for
scientific reasoning, coding, and tool use. This official Q8_0 GGUF
build uses llama.cpp and includes the vision projector.
Repository: localaiLicense: apache-2.0
lfm2.5-24b-a2b-turbo-brilliance-q8
DavidAU's Turbo Brilliance build of LFM2.5-24B-A2B, a text-only reasoning
model with 24B total parameters and 2.45B active parameters. This entry
uses the Q8_0 MAX GGUF quantization and the embedded Turbo Brilliance
chat template for multilingual chat and tool use.
Repository: localaiLicense: other
tess-4.5-27b-q8
Tess 4.5 is a 27B Qwen3.8 fine-tune for coding, reasoning, and tool use.
This Q8_0 GGUF build includes the F16 vision projector and uses
llama.cpp with the embedded chat template. Speculative decoding is disabled.
Repository: localaiLicense: apache-2.0
spark-x2.6-4b-q8
Spark-X2.6-4B is XHToken's 4B text model for conversation, reasoning,
coding, and multilingual tasks. This build uses Q8_0 GGUF weights,
the embedded Jinja chat template, and a 32K-token default context.
Repository: localaiLicense: apache-2.0
ornith-1.5-122b-q8
Ornith 1.5 122B is a Qwen3.5 mixture-of-experts model for reasoning,
coding, and tool use. This Q8_0 GGUF build includes the F16 vision
projector for image input and uses an 8,192-token default context.
Repository: localaiLicense: mit
bge-large-en-v1.5-q8-0
BGE-large-en-v1.5 is a 335M English embedding model for semantic search
and retrieval. This Q8_0 GGUF build produces 1024-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-base-en-v1.5-q8-0
BGE-base-en-v1.5 is a 109M English embedding model for semantic search
and retrieval. This Q8_0 GGUF build produces 768-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-small-en-v1.5-q8-0
BGE-small-en-v1.5 is a 33M English embedding model for semantic search
and retrieval. This Q8_0 GGUF build produces 384-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
diplodocus-preview-q8
Diplodocus preview 4 is OsaurusAI's K2 Horizon 7B fine-tune for documents,
spreadsheets, and tool-assisted tasks. This Q8_0 GGUF build uses the
embedded chat template and enables reasoning by default. Applications
must provide the tools; the model download does not include workspace tools.
Repository: localaiLicense: apache-2.0
atomic-lfm2.5-24b-a2b-uncensored-q8
AtomicChat's LFM2.5-24B-A2B derivative reduces refusal behavior in the
original weights. This text-only mixture-of-experts model has 24B total
parameters and supports multilingual reasoning and tool use.
This entry uses the AD-Q8_0 GGUF build with the edits baked in.
Repository: localaiLicense: other
adavar-3b-q8
AdaVaR-3B is a Qwen2.5-VL-3B-Instruct fine-tune for visual reasoning
through text or image-grounded coordinates. This Q8_0 GGUF build
includes the F16 vision projector. Adaptive reasoning uses the publisher's
system prompt and question suffix; see the model card for these prompts.
Repository: localaiLicense: apache-2.0
simplicio-27b-q8
Simplicio-27B is a Qwen3.8-27B fine-tune for code editing and software engineering.
This Q8_0 GGUF build uses llama.cpp and includes the F16 vision projector.
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-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
mimo-v2.6-distill-qwen-9b-q8
MiMo-V2.6-Distill-Qwen-9B is Xiaomi MiMo's 9B Qwen3.5 fine-tune for coding, agent tasks, and visual coding.
This Q8_0 GGUF build uses llama.cpp with the model's embedded chat template and includes the F16 vision projector.
Repository: localaiLicense: mit
qwen3.8-27b-fable-5-coding-distilled-q8
A 27B Qwen3.8 fine-tune for coding, debugging, and agentic reasoning.
This Q8_0 GGUF build uses llama.cpp, the embedded chat template, and the F16 vision projector.
Repository: localaiLicense: apache-2.0
thinkingcap-qwen3.8-27b-q8
ThinkingCap is a 27B Qwen3.8 fine-tune trained to reduce reasoning tokens, with text and image input.
This Q8_0 GGUF build uses llama.cpp, the embedded chat template, and the F16 vision projector.
Licensed under PolyForm Small Business 1.0.0 with the publisher's personal-use grant; see the model license for permitted use.
Repository: localaiLicense: polyform-small-business-1.0.0
hemmingway-1-q8
Hemmingway-1 is Altworld's English-first 27B text model, fine-tuned from Qwen3.8-27B for everyday messages and creative writing.
This Q8_0 GGUF build uses llama.cpp and the model's embedded chat template. Licensed under CC BY-NC 4.0; commercial use requires a separate agreement.
Repository: localaiLicense: cc-by-nc-4.0
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