Filter by type:
Filter by tags:
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
ling-3.0-flash-iq2
Ling-3.0-flash in the higher-quality 44.7 GB AD-IQ2_XS GGUF format. This
variant preserves more model fidelity for hosts with enough memory.
Repository: localaiLicense: mit
ornith-1.0-9b-q4
Ornith-1.0-9B is an MIT-licensed Qwen3.5 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 Q4_K_M GGUF and F16 vision projector. A
higher-quality Q8_0 model is available as a variant.
Repository: localaiLicense: mit
ornith-1.0-9b-q8
Ornith-1.0-9B in the higher-quality Q8_0 GGUF format, with the shared F16
vision projector for multimodal prompts.
Repository: localaiLicense: mit
ornith-1.5-9b-q4
Ornith-1.5-9B is an MIT-licensed Qwen3.5 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 Q4_K_M GGUF and BF16 vision projector. A
higher-quality Q8_0 model is available as a variant.
Repository: localaiLicense: mit
ornith-1.5-9b-q8
Ornith-1.5-9B in the higher-quality Q8_0 GGUF format, with the shared BF16
vision projector for multimodal prompts.
Repository: localaiLicense: mit
qwen3.8-27b-q4
Qwen3.8-27B is Qwen's dense 27B vision-language model for reasoning,
coding, tool use, and long-running agent tasks. It accepts text, images,
and video, and it supports a native context window of 262K tokens.
This default entry uses the official Q4_K_M GGUF and Q8_0 vision
projector. The linked variants add MTP speculative decoding or use the
higher-quality Q8_0 model.
Repository: localaiLicense: apache-2.0
qwen3.8-27b-q4-mtp
Qwen3.8-27B with the official Q4_K_M model and Q4_0 MTP draft model.
MTP speculative decoding can increase generation speed by proposing
multiple tokens for the target model to verify.
Repository: localaiLicense: apache-2.0
qwen3.8-27b-q8
Qwen3.8-27B in the official Q8_0 GGUF format. This variant provides higher
model fidelity for hosts with enough memory.
Repository: localaiLicense: apache-2.0
qwen3.8-27b-ridge
Qwen3.8-27B Ridge is a 3.69-bit mixed quantization that keeps the
Gated-DeltaNet state path at Q8_0 and preserves the embedded MTP head.
It reduces the model weights to 12.59 GB while retaining multimodal,
reasoning, coding, tool-use, and long-context capabilities.
Repository: localaiLicense: apache-2.0
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
qwen3.8-9b-q8
Qwen3.8-9B in the higher-quality Q8_0 GGUF format. This variant preserves
more model fidelity for hosts with enough memory.
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
qwen3.8-4b-q8
Qwen3.8-4B in the higher-quality Q8_0 GGUF format. This variant preserves
more model fidelity for hosts with enough memory.
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
qwen3.8-2b-q8
Qwen3.8-2B in the higher-quality Q8_0 GGUF format. This variant preserves
more model fidelity while remaining suitable for compact hosts.
Repository: localaiLicense: apache-2.0
instella-moe-16b-a3b-think
AMD Instella-MoE-16B-A3B-Think is a reasoning and instruction-following
mixture-of-experts model with 16 billion total parameters and 3 billion
active parameters. It supports long-form reasoning, chat, coding, and tool
use. This entry uses the Q4_K_M GGUF quantization.
Repository: localaiLicense: other
instella-moe-16b-a3b-think-q8
AMD Instella-MoE-16B-A3B-Think is a reasoning and instruction-following
mixture-of-experts model with 16 billion total parameters and 3 billion
active parameters. It supports long-form reasoning, chat, coding, and tool
use. This entry uses the near-lossless Q8_0 GGUF quantization.
Repository: localaiLicense: other
parable-granite-4.1-3b-claude-fable-5
# Parable-Granite-4.1-3B-Claude-Fable-5
Granite 4.1 3B fine-tuned on genuine Claude Fable 5 and GPT-5.5 agent
traces (planning, tool use, reasoning from real agent sessions).
Agent-flavored small model: terminal workflows, idiomatic code fixes,
explanations. v2 recipe: completion-masked SFT, replay mix, seed-averaged
weights. Published corpus and eval harness.
Repository: localaiLicense: apache-2.0
parable-qwen3-4b-claude-fable-5
# Parable-Qwen3-4B-Claude-Fable-5
Qwen3 4B fine-tuned on genuine Claude Fable 5 agent traces. Thinking-mode
reasoning, agent/terminal task flavor, tool-call formatting.
Repository: localaiLicense: apache-2.0
parable-granite-4.1-8b-claude-fable-5
# Parable-Granite-4.1-8B-Claude-Fable-5
Granite 4.1 8B fine-tuned on genuine Claude Fable 5 and GPT-5.5 agent
traces. Strongest Parable model: multi-step scripts, configs, terminal
workflows, reasoning.
Repository: localaiLicense: apache-2.0
Page 1