Skip to page content
SAVRN
Search Plan a project

Open-weight model · Any to any

Qwen3.8-27B-BF16-SSMFIX-apostate-GSQ-RCO-IQ3_S

by Sky redashes/Qwen3.8-27B-BF16-SSMFIX-apostate-GSQ-RCO-IQ3_S

Qwen3.8-27B-BF16-SSMFIX-apostate-GSQ-RCO-IQ3_S is an open-weight model for any to any from Sky, released under Apache License 2.0. Its published files total 12.0 GB.

Qwen3.8-27B (bf16 source, ssmfix chat template applied) quantized with a per-tensor mixed scheme derived from GSQ grid learning + RCO Riemannian tangent-projection search.

Parameters—
Context—
Weights12.0 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—

Model Card

By Sky, published under apache-2.0, revision 1279fbced1e0.

Qwen3.8-27B (bf16 source, ssmfix chat template applied) quantized with a per-tensor mixed scheme derived from GSQ grid learning + RCO Riemannian tangent-projection search. Search domain {2,3,4} bit, target avg ~3.5 bpw; result: IQ3S-dominant mix (IQ3S=144, IQ4XS=96, IQ3XXS=78 tensors), avg bpw 3.52, 866 tensors, 12.0 GB. Built with llama-quantize + imatrix (fineweb-edu style corpus) from the bf16 -> f16 GGUF (converthftogguf.py). Includes MTP head; pair with mmproj-Qwen3.8-27B-Q5K-MIX for vision. Measured on RTX 3080 20G (llama-kvmem-server, kv q80, draft-mtp n=3): decode ~48.5 t/s, prefill ~1150 t/s.

Read Sky's full model card

Qwen3.8-27B (bf16 source, ssmfix chat template applied) quantized with a per-tensor mixed scheme derived from GSQ grid learning + RCO Riemannian tangent-projection search. Search domain {2,3,4} bit, target avg ~3.5 bpw; result: IQ3_S-dominant mix (IQ3_S=144, IQ4_XS=96, IQ3_XXS=78 tensors), avg bpw 3.52, 866 tensors, 12.0 GB.

Built with llama-quantize + imatrix (fineweb-edu style corpus) from the bf16 -> f16 GGUF (convert_hf_to_gguf.py). Includes MTP head; pair with mmproj-Qwen3.8-27B-Q5_K-MIX for vision.

Measured on RTX 3080 20G (llama-kvmem-server, kv q8_0, draft-mtp n=3): decode ~48.5 t/s, prefill ~1150 t/s.

Identity and Version

Repository
redashes/Qwen3.8-27B-BF16-SSMFIX-apostate-GSQ-RCO-IQ3_S
Publisher
Sky
Task
Any to any
Modality
Multimodal
Library
Not stated by the source
Parameters
Not stated by the source
Languages
gsq, rco
Revision
1279fbced1e0b310ff56a168e4a0f8d9333795c2
First published
2026-09-30
Last updated
2026-09-30

Files and Weights

3 files, 12.0 GB in total. The weights are 1 file totalling 12.0 GB in gguf.

Weights1 file · 12.0 GB
Documentation1 file · 799 B
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
Qwen3.8-27B-GSQstyle-IQ3S-mtp.ggufWeights12.0 GB f5d6d6afc5c9
README.mdDocumentation799 B —
.gitattributesRepository1.6 KB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
12.0 GB
Download from Sky

Released by Sky through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published12.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About Qwen3.8-27B-BF16-SSMFIX-apostate-GSQ-RCO-IQ3_S

Can I use Qwen3.8-27B-BF16-SSMFIX-apostate-GSQ-RCO-IQ3_S commercially?

Yes. Qwen3.8-27B-BF16-SSMFIX-apostate-GSQ-RCO-IQ3_S is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

Similar Models

Model · Any to any

gemma-4-12B-it-qat-GGUF

Unsloth AI

This model ships a Multi-Token Prediction drafter at the repo root (mtp-gemma-4-12B-it.gguf, a near-lossless smart Q40). A recent llama.cpp auto-discovers it from -hf, so you do not pass --model-draft: The drafter shares the target's KV cache and does not change the output (the target verifies every drafted token). See the MTP/ folder for the other precisions and explicit usage. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context…

Open weights apache-2.0 transformers

Model · Any to any

gemma-4-E4B-it-GGUF

GGML Org

Run with https://llama.app - https://huggingface.co/google/gemma-4-E4B-it - https://huggingface.co/google/gemma-4-E4B-it-assistant - https://huggingface.co/google/gemma-4-E4B-it-qat-q40-unquantized-assistant - https://huggingface.co/google/gemma-4-E4B-it-qat-q40-unquantized - add info - add dflash

Open weights apache-2.0

Model · Any to any

gemma-4-12B-it-qat-q4_0-gguf

Google

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from…

Open weights apache-2.0 transformers

Model · Any to any

gemma-4-E4B-it-qat-q4_0-gguf

Google

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from…

Open weights apache-2.0

Model · Any to any

gemma-4-E4B-it-qat-GGUF

Unsloth AI

This model ships a Multi-Token Prediction drafter at the repo root (mtp-gemma-4-E4B-it.gguf, a near-lossless smart Q40). A recent llama.cpp auto-discovers it from -hf, so you do not pass --model-draft: The drafter shares the target's KV cache and does not change the output (the target verifies every drafted token). See the MTP/ folder for the other precisions and explicit usage. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context…

Open weights apache-2.0 131,072 tokens transformers

Model · Any to any

gemma-4-E2B-it-qat-GGUF

Unsloth AI

This model ships a Multi-Token Prediction drafter at the repo root (mtp-gemma-4-E2B-it.gguf, a near-lossless smart Q40). A recent llama.cpp auto-discovers it from -hf, so you do not pass --model-draft: The drafter shares the target's KV cache and does not change the output (the target verifies every drafted token). See the MTP/ folder for the other precisions and explicit usage. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context…

Open weights apache-2.0 131,072 tokens transformers