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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
adavar-3b-q4
AdaVaR-3B is a Qwen2.5-VL-3B-Instruct fine-tune for visual reasoning
through text or image-grounded coordinates. This Q4_K_M 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
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
llm-jp-4-33b-thinking-q4
LLM-jp-4-33B-thinking is an Apache-2.0 Japanese and English reasoning
model from Japan's National Institute of Informatics. Its dense Llama
architecture has 33 billion parameters and a 65K-token context window.
The model was aligned with supervised fine-tuning and DPO for multi-turn
conversation and instruction following.
This default entry uses the 20.2 GB Q4_K_M GGUF. The official 66.4 GB
BF16 weights are available as a higher-fidelity variant.
Repository: localaiLicense: apache-2.0
llm-jp-4-33b-thinking-bf16
LLM-jp-4-33B-thinking in the official 66.4 GB BF16 GGUF format. This
variant preserves the original model precision for hosts with enough
memory.
Repository: localaiLicense: apache-2.0
granite-4.2-3b-q4
IBM Granite 4.2 3B is a compact multilingual reasoning model for chat,
coding, long-context tasks, and tool use. This entry uses the Q4_K_M
GGUF; a higher-fidelity Q8_0 build is available as a variant.
Repository: localaiLicense: apache-2.0
granite-4.2-3b-q8
IBM Granite 4.2 3B in the higher-fidelity Q8_0 GGUF format. It is a
compact multilingual reasoning model for chat, coding, and tool use.
Repository: localaiLicense: apache-2.0
byteshape-qwen3.8-27b-iq3-s
ByteShape's ShapeLearn quantization of Qwen3.8 27B for chat, reasoning,
coding, and image input. This IQ3_S-3.23bpw build uses mixed tensor
precisions and includes a BF16 vision projector. MTP decoding is enabled.
Repository: localaiLicense: apache-2.0
carbon-3b-q4
Carbon-3B is Hugging Face's 3B-parameter genomic foundation model for DNA
and RNA sequence generation, recovery, variant-effect prediction, and
motif-perturbation analysis. It supports 32,768 tokens natively and uses a
hybrid tokenizer with 6-mer DNA tokens.
This default entry uses the Q4_K_M GGUF. A higher-quality Q8_0 build is
available as a variant. Prefix DNA sequences with `` and use uppercase
A, C, G, and T characters in groups of six.
Repository: localaiLicense: apache-2.0
carbon-3b-q8
Carbon-3B in the higher-quality Q8_0 GGUF format for genomic sequence
generation and analysis.
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.
Repository: localaiLicense: mit
ornith-1.5-35b-a3b-apex-compact
Ornith-1.5-35B-A3B in the smaller APEX Compact GGUF format, with the
shared BF16 vision projector for multimodal prompts.
Repository: localaiLicense: mit
ornith-1.5-35b-a3b-mtp-apex
Ornith-1.5-35B-A3B in the APEX Balanced GGUF format with native
multi-token prediction enabled for speculative decoding, plus the shared
BF16 vision projector.
Repository: localaiLicense: mit
ornith-1.5-35b-a3b-mtp-apex-compact
Ornith-1.5-35B-A3B in the APEX Compact GGUF format with native multi-token
prediction enabled for speculative decoding, plus the shared BF16 vision
projector.
Repository: localaiLicense: mit
ornith-1.5-35b-a3b-q4
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 activates about 3B parameters per token and
supports text and image input with a context window of 262K tokens.
This default entry uses the Q4_K_M GGUF and BF16 vision projector.
Q5_K_M, Q6_K, and Q8_0 builds are available as variants.
Repository: localaiLicense: mit
ornith-1.5-35b-a3b-q8
Ornith-1.5-35B-A3B in the higher-quality Q8_0 GGUF format, with the shared
BF16 vision projector for multimodal prompts.
Repository: localaiLicense: mit
ornith-1.5-35b-a3b-q5
Ornith-1.5-35B-A3B in the Q5_K_M GGUF format, with the shared
BF16 vision projector for multimodal prompts.
Repository: localaiLicense: mit
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