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tiel-coder-35b-a3b-q4
Tiel-Coder-35B-A3B is a 35B-parameter mixture-of-experts model for coding,
reasoning, tool use, and vision tasks. This default entry uses the
Q4_K_XL GGUF and BF16 vision projector.
Repository: localaiLicense: mit
tiel-coder-35b-a3b-q4-mtp
Tiel-Coder-35B-A3B in Q4_K_XL format with MTP speculative decoding and a
BF16 vision projector.
Repository: localaiLicense: mit
tiel-coder-35b-a3b-q8
Tiel-Coder-35B-A3B in the higher-quality Q8_K_XL GGUF format, with the
BF16 vision projector for multimodal prompts.
Repository: localaiLicense: mit
muse-glimmer-30b
Muse Glimmer is Meta Superintelligence Labs' Apache-2.0 dense 30B model for
autonomous agentic work, coding, tool use, long-horizon reasoning, and
multimodal understanding. It supports more than 100 languages, interleaved
text and image input through its 1.8B-parameter perception encoder, and a
131K-token context window.
This entry uses the publisher's higher-quality dynamic K-quant GGUF and
official quantized vision projector. Automatic variant selection can use
the smaller 17 GB quantization or a DFlash-accelerated build when it fits.
Repository: localaiLicense: apache-2.0
muse-glimmer-30b-dflash
Muse Glimmer's higher-quality dynamic K-quant GGUF with the official
quantized perception encoder and DFlash drafter. DFlash proposes blocks of
up to 16 tokens for the target to verify in parallel, accelerating output
without changing model quality. Flash attention is enabled for this path.
Repository: localaiLicense: apache-2.0
muse-glimmer-30b-17gb
Muse Glimmer's smaller 17 GB K-quant GGUF with the official quantized
perception encoder. It preserves the model's agentic, coding, tool-use,
multilingual, and image-understanding capabilities for hosts with less
memory than the dynamic quantization requires.
Repository: localaiLicense: apache-2.0
muse-glimmer-30b-17gb-dflash
Muse Glimmer's smaller 17 GB K-quant GGUF with the official quantized
perception encoder and DFlash drafter. This is the lowest-memory published
build that retains image understanding and block-speculative decoding.
Flash attention is enabled for the DFlash path.
Repository: localaiLicense: apache-2.0
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
north-mini-code-1.0
North Mini Code 1.0 is Cohere Labs' Apache-2.0 sparse mixture-of-experts
coding model with 30B total parameters and 3B active parameters. It targets
code generation, agentic software engineering, terminal tasks, tool use,
and interleaved reasoning with a 256K-token context window. This entry uses
the UD-Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
north-mini-code-1.0-q8
North Mini Code 1.0 is Cohere Labs' Apache-2.0 sparse mixture-of-experts
coding model with 30B total parameters and 3B active parameters. It targets
code generation, agentic software engineering, terminal tasks, tool use,
and interleaved reasoning with a 256K-token context window. This entry uses
the Q8_0 GGUF quantization.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-q8
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry uses the higher-quality Q8_0 GGUF quantization.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-i-quality
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-i-balanced
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-i-compact
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-i-mini
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-quality
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-balanced
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-compact
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
qwopus3.6-27b-fusion
Qwopus3.6-27B Fusion is an experimental Qwen3.6-27B merge that combines
reasoning and code-execution fine-tunes. It targets agentic coding,
mathematics, tool use, and long-context work while retaining image input.
This default entry uses the Q4_K_M GGUF quantization and the shared Q8_0
vision projector.
Repository: localaiLicense: qwen
qwopus3.6-27b-fusion-q8
Qwopus3.6-27B Fusion is an experimental Qwen3.6-27B reasoning and coding
merge. This entry uses the near-lossless Q8_0 GGUF quantization and the
shared Q8_0 vision projector.
Repository: localaiLicense: qwen
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