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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
supra2-100m-instruct
Supra2-100M-Instruct is a compact English chat model trained from scratch by
SupraLabs on the Qwen3 architecture. It has 100 million parameters, a
2,048-token context window, and is intended for lightweight experiments and
constrained edge deployments. This entry uses the publisher's official F16
GGUF build.
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
dfm-mimir:vllm
DFM Mimir is an Apache-2.0, instruction-tuned HRM-Text model from Danish
Foundation Models. It has about 1 billion parameters and a 4,096-token
context window. The model focuses on Danish and English chat, reasoning,
mathematics, and code generation, and uses only permissible post-training
data. This entry serves the official BF16 safetensors checkpoint with vLLM.
Repository: localaiLicense: apache-2.0
ling-3.0-tiny-q4
Ling-3.0-tiny is InclusionAI's MIT-licensed hybrid reasoning MoE model
with 7.9B total parameters and 1.3B active parameters per token. It
targets reasoning, coding, instruction following, and agentic tasks with
a native 131K-token context window.
This default entry uses the Q4_K_M GGUF. A higher-quality Q8_0 model is
available as a variant.
Repository: localaiLicense: mit
hy-mt2-7b-q4
Hy-MT2-7B is Tencent's 7B multilingual translation model. It supports
translation instructions across 33 languages, including terminology
control and style transfer. This Q4_K_M GGUF uses the embedded chat
template with an 8K context window. Include the target language in
your prompt.
Repository: localaiLicense: apache-2.0
hy-mt2-7b-q6
Hy-MT2-7B is Tencent's 7B multilingual translation model. It supports
translation instructions across 33 languages, including terminology
control and style transfer. This Q6_K GGUF uses the embedded chat
template with an 8K context window. Include the target language in
your prompt.
Repository: localaiLicense: apache-2.0
hy-mt2-7b-q8
Hy-MT2-7B is Tencent's 7B multilingual translation model. It supports
translation instructions across 33 languages, including terminology
control and style transfer. This Q8_0 GGUF uses the embedded chat
template with an 8K context window. Include the target language in
your prompt.
Repository: localaiLicense: apache-2.0
hy-mt2-1.8b-q4
Hy-MT2-1.8B is Tencent's compact multilingual translation model. It
follows translation instructions across 33 languages and supports tasks
such as terminology control, style transfer, and structure-preserving
translation.
This default entry uses the 1.1 GB Q4_K_M GGUF. A higher-quality Q8_0
model is available as a variant.
Repository: localaiLicense: apache-2.0
ling-3.0-flash-iq1
Ling-3.0-flash is InclusionAI's MIT-licensed hybrid reasoning MoE model
with 124B total parameters and 5.5B active parameters per token. It targets
coding, deep research, instruction following, and agentic workflows with a
native 256K-token context window.
This default entry uses the 36.5 GB AD-IQ1_M GGUF. A higher-quality
44.7 GB AD-IQ2_XS model is available as a variant.
Repository: localaiLicense: mit
qwen3.8-9b-q4
Qwen3.8-9B is Empero AI's full-parameter distillation of Qwen3.8 2.4T
A95B into the dense Qwen3.5-9B architecture. It targets reasoning,
mathematics, coding, instruction following, and tool use, and supports a
native 262K-token context window. This default entry uses Q4_K_M weights;
a higher-quality Q8_0 build is available as a variant.
Repository: localaiLicense: apache-2.0
qwen3.8-4b-q4
Qwen3.8-4B is Empero AI's full-parameter distillation of Qwen3.8 2.4T
A95B into the Qwen3.5-4B architecture. It targets mathematics, reasoning,
instruction following, and tool use with a native 262K-token context
window. This default entry uses Q4_K_M weights; a higher-quality Q8_0
build is available as a variant.
Repository: localaiLicense: apache-2.0
qwen3.8-2b-q4
Qwen3.8-2B is Empero AI's smallest Qwen3.8 reasoning distillation. It uses
the Qwen3.5-2B architecture and targets mathematics, instruction
following, tool use, and edge deployment with a native 262K-token context
window. This default entry uses Q4_K_M weights; a higher-quality Q8_0
build is available as a variant.
Repository: localaiLicense: apache-2.0
homura-30b-q4
Homura 30B is an English, agent-focused fine-tune of Muse Glimmer 30B.
It targets autonomous tool use and direct instruction following. This
entry uses the publisher's 16.9 GB Q4_K_M GGUF and supports a 131K-token
context window.
Repository: localaiLicense: apache-2.0
qwen3.8-2b-distill-q4
Qwen3.8 2B Distill is an Apache-2.0, text-only Qwen3.5 2B fine-tune
distilled from Qwen3.8 2.4T A95B reasoning traces. It targets compact
reasoning, coding, instruction following, and function calling with a 262K
native context window. This entry uses the balanced Q4_K_M GGUF
quantization; the Q8_0 variant offers higher fidelity.
Repository: localaiLicense: apache-2.0
qwen3.8-2b-distill-q8
Qwen3.8 2B Distill in the higher-fidelity Q8_0 GGUF format. This text-only
Qwen3.5 2B fine-tune targets reasoning, coding, instruction following, and
function calling with a 262K native context window.
Repository: localaiLicense: apache-2.0
qwen3.8-4b-distill-q4
Qwen3.8 4B Distill is an Apache-2.0, text-only Qwen3.5 4B fine-tune
distilled from Qwen3.8 2.4T A95B reasoning traces. It targets reasoning,
coding, instruction following, and function calling with a 262K native
context window. This entry uses the balanced Q4_K_M GGUF quantization; the
Q8_0 variant offers higher fidelity.
Repository: localaiLicense: apache-2.0
qwen3.8-4b-distill-q8
Qwen3.8 4B Distill in the higher-fidelity Q8_0 GGUF format. This text-only
Qwen3.5 4B fine-tune targets reasoning, coding, instruction following, and
function calling with a 262K native context window.
Repository: localaiLicense: apache-2.0
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