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pocket-35b
POCKET-35B is an Apache-2.0 Qwen3.5-family mixture-of-experts model from
FINAL-Bench/VIDRAFT, derived from Darwin-36B-Opus and packaged for stock
llama.cpp. This entry uses the quality-oriented Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
pocket-35b-q2
POCKET-35B is an Apache-2.0 Qwen3.5-family mixture-of-experts model from
FINAL-Bench/VIDRAFT, derived from Darwin-36B-Opus and packaged for stock
llama.cpp. This entry uses the smaller Q2_K GGUF quantization.
Repository: localaiLicense: apache-2.0
pocket-35b-iq1
POCKET-35B is an Apache-2.0 Qwen3.5-family mixture-of-experts model from
FINAL-Bench/VIDRAFT, derived from Darwin-36B-Opus and packaged for stock
llama.cpp. This entry uses the most compact IQ1_M GGUF quantization.
Repository: localaiLicense: apache-2.0
pocket-26b
POCKET-26B is an Apache-2.0 Gemma 4 26B-A4B mixture-of-experts model from
FINAL-Bench/VIDRAFT, tuned for Korean and packaged for stock llama.cpp.
This entry uses the quality-oriented Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
pocket-26b-q2
POCKET-26B is an Apache-2.0 Gemma 4 26B-A4B mixture-of-experts model from
FINAL-Bench/VIDRAFT, tuned for Korean and packaged for stock llama.cpp.
This entry uses the smaller Q2_K GGUF quantization.
Repository: localaiLicense: apache-2.0
mellum2-12b-a2.5b-instruct
Mellum2-12B-A2.5B-Instruct is an Apache-2.0 mixture-of-experts model from
JetBrains with 12 billion total parameters, 2.5 billion activated per token,
and a 131,072-token context window.
This entry uses the Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
mellum2-12b-a2.5b-instruct-q8
Mellum2-12B-A2.5B-Instruct is an Apache-2.0 mixture-of-experts model from
JetBrains with 12 billion total parameters, 2.5 billion activated per token,
and a 131,072-token context window.
This entry uses the higher-quality Q8_0 GGUF quantization.
Repository: localaiLicense: apache-2.0
laguna-xs-2.1
Laguna XS 2.1 is Poolside's 33B-parameter, 3B-active Mixture-of-Experts model for agentic coding and long-horizon work on local machines. It supports tool use, interleaved reasoning, and a native 262K-token context window. This default entry uses the official 20.3 GB Q4_K_M GGUF.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-i-quality
Laguna XS 2.1 in the 21.8 GB APEX-I Quality format. This is the highest-fidelity importance-matrix APEX build for llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-i-balanced
Laguna XS 2.1 in the 24.3 GB APEX-I Balanced format, an importance-matrix build balancing fidelity and memory use for llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-i-compact
Laguna XS 2.1 in the 15.8 GB APEX-I Compact format, an importance-matrix build tuned for lower memory use in llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-i-mini
Laguna XS 2.1 in the 12.8 GB APEX-I Mini format, the smallest importance-matrix APEX build for llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-quality
Laguna XS 2.1 in the 21.8 GB APEX Quality format, the highest-fidelity non-imatrix APEX build for llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-balanced
Laguna XS 2.1 in the 24.3 GB APEX Balanced format, balancing fidelity and memory use for llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-compact
Laguna XS 2.1 in the 15.8 GB APEX Compact format, tuned for lower memory use in llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-s-2.1-q8
Laguna S 2.1 is Poolside's 118B-parameter, 8B-active Mixture-of-Experts model for agentic software engineering. It supports tool use and a native one-million-token context window; the official GGUF recommends 256K context for best output quality. This entry uses the 129 GB Q8_0 build, with routed experts quantized to Q8_0 and the signal path kept in BF16.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-s-2.1
Laguna S 2.1 is Poolside's 118B-parameter, 8B-active Mixture-of-Experts model for agentic software engineering. It supports tool use and a native one-million-token context window; the official GGUF recommends 256K context for best output quality. This default entry uses the current 96 GB Q4_K_M artifact, with imatrix-quantized routed experts and a Q8_0 signal path.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-s-2.1-dflash
Laguna S 2.1's 96 GB Q4_K_M target paired with Poolside's 2.2 GB BF16 DFlash drafter for speculative decoding. DFlash drafts a block of tokens per forward pass and accelerates generation without changing the target model's outputs. Flash attention is enabled because the DFlash path requires it.
License: OpenMDW 1.1.
Repository: localaiLicense: other
qwen3.6-35b-a3b-dflash
Qwen3.6-35B-A3B (Mixture-of-Experts, ~3B active per token) paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend. DFlash speedups on MoE targets are smaller than on dense models, but still useful.
DFlash produces a whole block of draft tokens in a single forward pass and injects the target model's hidden states into the drafter's attention, which keeps the drafter tiny while making drafting GPU-friendly. The UD-Q4_K_M file carries the full Qwen3.6-35B-A3B target; the ~0.4 GB Q8_0 drafter (`draft-dflash`) accelerates generation without changing the target's outputs. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here.
Flash attention is required for DFlash and is enabled in this config. A GPU is recommended.
License: Apache 2.0.
Repository: localaiLicense: apache-2.0
gemma-4-26b-a4b-it-qat-q4_0
Gemma 4 26B-A4B is a multimodal (text + image) instruction-tuned Mixture-of-Experts model from Google DeepMind, optimized with Quantization-Aware Training (QAT) to preserve bfloat16-level quality at a fraction of the memory. With 26B total parameters and ~4B active per token, it delivers large-model quality at a much lower inference cost. This is the official Google Q4_0 GGUF, shipped with its multimodal projector.
License: Apache 2.0 | Authors: Google DeepMind
Repository: localaiLicense: apache-2.0
qwen3.6-40b-claude-4.6-opus-deckard-heretic-uncensored-thinking-neo-code-di-imatrix-max
The Qwen 3.5 version (also 40B) got 181 likes+ This version uses the new Qwen 3.6 27B arch (which exceeds even Qwen's own 398B model).
WARNING: This model has character and intelligence. It will take no prisoners. It will give no quarter. Uncensored,
Unfiltered and boldly confident. Not even remotely "SFW", if you ask it for NSFW content. And it is wickedly smart too - exceeding the base model in 6 out of 7 benchmarks.
Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking
40 billion parameters (dense, not moe) expanded from 27B Qwen 3.6, then trained on Claude 4.6 Opus High Reasoning dataset via Unsloth on local hardware... but there
is much more to the story - in comes DECKARD.
96 layers, 1275 Tensors. (50% more than base model of 27B)
Features variable length reasoning ; less complex = shorter, longer for more complex.
Model performance has increased dramatically. And it has character too.
A lot of character.
No censorship, no nanny. (via Heretic)
And it is very, very smart.
...
Repository: localaiLicense: apache-2.0
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