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sage-a1.5-q4
Sage-A1.5 is a 35B mixture-of-experts model based on Qwen3.6 for
scientific reasoning, coding, and tool use. This official Q4_K_M GGUF
build uses llama.cpp and includes the vision projector.
Repository: localaiLicense: apache-2.0
sage-a1.5-q8
Sage-A1.5 is a 35B mixture-of-experts model based on Qwen3.6 for
scientific reasoning, coding, and tool use. This official Q8_0 GGUF
build uses llama.cpp and includes the vision projector.
Repository: localaiLicense: apache-2.0
lfm2.5-24b-a2b-turbo-brilliance-iq4-xs
DavidAU's Turbo Brilliance build of LFM2.5-24B-A2B, a text-only reasoning
model with 24B total parameters and 2.45B active parameters. This entry
uses the IQ4_XS GGUF quantization and the embedded Turbo Brilliance
chat template for multilingual chat and tool use.
Repository: localaiLicense: other
lfm2.5-24b-a2b-turbo-brilliance-q8
DavidAU's Turbo Brilliance build of LFM2.5-24B-A2B, a text-only reasoning
model with 24B total parameters and 2.45B active parameters. This entry
uses the Q8_0 MAX GGUF quantization and the embedded Turbo Brilliance
chat template for multilingual chat and tool use.
Repository: localaiLicense: other
tess-4.5-27b-q4
Tess 4.5 is a 27B Qwen3.8 fine-tune for coding, reasoning, and tool use.
This Q4_K_M GGUF build includes the F16 vision projector and uses
llama.cpp with the embedded chat template. Speculative decoding is disabled.
Repository: localaiLicense: apache-2.0
tess-4.5-27b-q6
Tess 4.5 is a 27B Qwen3.8 fine-tune for coding, reasoning, and tool use.
This Q6_K GGUF build includes the F16 vision projector and uses
llama.cpp with the embedded chat template. Speculative decoding is disabled.
Repository: localaiLicense: apache-2.0
tess-4.5-27b-q8
Tess 4.5 is a 27B Qwen3.8 fine-tune for coding, reasoning, and tool use.
This Q8_0 GGUF build includes the F16 vision projector and uses
llama.cpp with the embedded chat template. Speculative decoding is disabled.
Repository: localaiLicense: apache-2.0
spark-x2.6-4b-q4
Spark-X2.6-4B is XHToken's 4B text model for conversation, reasoning,
coding, and multilingual tasks. This build uses Q4_K_M GGUF weights,
the embedded Jinja chat template, and a 32K-token default context.
Repository: localaiLicense: apache-2.0
spark-x2.6-4b-q8
Spark-X2.6-4B is XHToken's 4B text model for conversation, reasoning,
coding, and multilingual tasks. This build uses Q8_0 GGUF weights,
the embedded Jinja chat template, and a 32K-token default context.
Repository: localaiLicense: apache-2.0
qwen3.8-27b-defiance-iq4
Defiance combines HauhauCS's uncensored Qwen3.8-27B weights with a
customized Turbo Brilliance chat template for reasoning and roleplay.
This IQ4_XS GGUF build uses llama.cpp and the embedded template,
with a 32,768-token context. Text input only; MTP is not enabled.
Repository: localaiLicense: apache-2.0
qwen3.8-27b-defiance-q6
Defiance combines HauhauCS's uncensored Qwen3.8-27B weights with a
customized Turbo Brilliance chat template for reasoning and roleplay.
This Q6_K_P GGUF build uses llama.cpp and the embedded template,
with a 32,768-token context. Text input only; MTP is not enabled.
Repository: localaiLicense: apache-2.0
ornith-1.5-122b-ud-q4-k-xl
Ornith 1.5 122B is a Qwen3.5 mixture-of-experts model for reasoning,
coding, and tool use. This UD-Q4_K_XL GGUF build includes the F16 vision
projector for image input and uses an 8,192-token default context.
Repository: localaiLicense: mit
ornith-1.5-122b-q8
Ornith 1.5 122B is a Qwen3.5 mixture-of-experts model for reasoning,
coding, and tool use. This Q8_0 GGUF build includes the F16 vision
projector for image input and uses an 8,192-token default context.
Repository: localaiLicense: mit
deepseek-v4.1-flash-1017
# DeepSeek-V4.1-Flash-1017
Technical Report 👁️
## Introduction
**DeepSeek-V4.1-Flash-1017** is a minor update to **DeepSeek-V4.1-Flash**, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks. Alongside these gains, the model reduces task execution time and output length, enabling users to complete tasks faster and at lower cost and advancing the performance–cost Pareto frontier across multiple agent harnesses.
*† denotes the text-only subset of HLE. We use the Claude Code harness for Terminal-Bench 4.0 and the Mini-SWE-V2 harness for DeepSWE v1.1.*
## Prompt Encoding
This release does not include a Jinja-format chat template. The `encoding` folder contains a self-contained Python reference implementation (`encoding.py`) with test cases for multi-turn conversations, tool calling, thinking mode, numeric reasoning effort, mid-conversation system messages, and interleaved image content.
...
Repository: localaiLicense: mit
lfm2.5-24b-a2b-ud-q4
LiquidAI LFM2.5-24B-A2B is a text-only reasoning model with 24B total
parameters and 2.45B active parameters for multilingual chat and tool use.
This entry uses Unsloth's dynamic UD-Q4_K_XL GGUF quantization.
Repository: localaiLicense: other
lfm2.5-24b-a2b-ud-q6
LiquidAI LFM2.5-24B-A2B is a text-only reasoning model with 24B total
parameters and 2.45B active parameters for multilingual chat and tool use.
This entry uses Unsloth's dynamic UD-Q6_K_XL GGUF quantization.
Repository: localaiLicense: other
ternary-bonsai-2-2b
Ternary Bonsai 2 2B is a vision-language model derived from Qwen3.5-2B
with ternary transformer weights. This PQ2_0 build uses the Bonsai
backend and includes the Q8_0 vision projector. The three standard
packings contain the same weights in different storage layouts.
Repository: localaiLicense: apache-2.0
ternary-bonsai-2-2b-ptq1
Ternary Bonsai 2 2B is a vision-language model derived from Qwen3.5-2B
with ternary transformer weights. This PTQ1_0 build uses the Bonsai
backend and includes the Q8_0 vision projector. The three standard
packings contain the same weights in different storage layouts.
Repository: localaiLicense: apache-2.0
ternary-bonsai-2-2b-q2
Ternary Bonsai 2 2B is a vision-language model derived from Qwen3.5-2B
with ternary transformer weights. This Q2_0 build uses the Bonsai
backend and includes the Q8_0 vision projector. The three standard
packings contain the same weights in different storage layouts.
Repository: localaiLicense: apache-2.0
ternary-bonsai-2-9b
Ternary Bonsai 2 9B is a vision-language model derived from Qwen3.5-9B
with ternary transformer weights. This PQ2_0 build uses the Bonsai
backend and includes the Q8_0 vision projector. The three standard
packings contain the same weights in different storage layouts.
Repository: localaiLicense: apache-2.0
ternary-bonsai-2-9b-ptq1
Ternary Bonsai 2 9B is a vision-language model derived from Qwen3.5-9B
with ternary transformer weights. This PTQ1_0 build uses the Bonsai
backend and includes the Q8_0 vision projector. The three standard
packings contain the same weights in different storage layouts.
Repository: localaiLicense: apache-2.0
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