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ternary-bonsai-2-27b
Ternary Bonsai 2 27B (PrismML) is a 27B-class reasoning model with ternary
transformer weights. This PTQ1_0 build packs the trits densely at 1.75 bits
per weight (5.95 GB) and includes the Q8_0 vision projector. PTQ1_0 is a
Prism-private GGUF type, so the entry uses the bonsai backend (PrismML's
llama.cpp fork) instead of stock llama.cpp.
Repository: localaiLicense: apache-2.0
swift-qwen3.8-27b
Swift-Qwen3.8-27B is UkisAI's reasoning-efficient fine-tune of Qwen3.8-27B.
The publisher reports 58.3% fewer thinking tokens with less than 1% quality loss.
This Q4_K_M GGUF includes the F16 vision projector and enables MTP speculative decoding.
The weights use the Swift Open License v1.0.
Repository: localaiLicense: swift-open-license-1.0
ornith-1.5-9b-uncensored
# Ornith-1.5-9B-uncensored
An **abliterated** (refusal-direction-ablated) build of
`ornith-ai/Ornith-1.5-9B`, produced with
ZeroFuse and published by
junafinity.
This is the **9B control checkpoint** (bf16). Mac users should start from the MLX-8bit or GGUF-8bit siblings. The official 9B base has **no `mtp.*` tensors**; nothing was grafted.
**Vision tower and MTP heads are preserved** — see
Vision & MTP preservation for the before/after audit.
## Intended use: red teaming and defensive cybersecurity research
These uncensored (abliterated) weights are built as a **research instrument** for red teaming and defensive cybersecurity work. Safety training suppresses the *display* of capability, not capability itself. A refusal tells you the model declined. It does not tell you whether the weights could have complied. That conflation underestimates the true ceiling and hides holes in *your* filters, classifiers, and policy layer.
Use each uncensored checkpoint as the **treatment half of a controlled pair** against its original base model:
...
Repository: localaiLicense: apache-2.0
deepseek-v4-flash-vision-exp
# DeepSeek-V4-Flash-Vision-Exp
## Introduction
We are excited to introduce **DeepSeek-V4-Flash-Vision-Exp**, our first experimental multimodal model in the DeepSeek-V4 family. It builds on the DeepSeek-V4-Flash architecture by incorporating visual modules and undergoing continued training to unlock visual understanding capabilities.
Compared to DeepSeek-V4-Flash-0731, DeepSeek-V4-Flash-Vision-Exp achieves substantial improvements on its multimodal agent capabilities, while maintaining comparable performance on text-only agent tasks.
Notes:
1. For the text agent benchmarks above, DeepSeek models are evaluated with the minimal mode of DeepSeek Harness as the agent framework, using the `max` reasoning effort level with `temperature = 1.0, top_p = 0.95`.
2. † For ApexBench and Agents' Last Exam, DeepSeek-V4-Flash-0731 ignores the multimodal elements in the input.
## Repository layout
This repository contains the tokenizer, prompt encoding reference, and a
minimal PyTorch inference implementation for DeepSeek-V4 Flash Vision. The
reference inference covers the vision encoder and aligner, DFlash attention,
MoE, Hyper-Connections, and the DSpark forward path.
...
Repository: localaiLicense: mit
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
mimo-v2.6-distill-qwen-9b
MiMo-V2.6-Distill-Qwen-9B is Xiaomi MiMo's 9B Qwen3.5 fine-tune for coding, agent tasks, and visual coding.
This Q4_K_M GGUF build uses llama.cpp with the model's embedded chat template and includes the F16 vision projector.
Repository: localaiLicense: mit
mimo-v2.6-distill-qwen-9b-q8
MiMo-V2.6-Distill-Qwen-9B is Xiaomi MiMo's 9B Qwen3.5 fine-tune for coding, agent tasks, and visual coding.
This Q8_0 GGUF build uses llama.cpp with the model's embedded chat template and includes the F16 vision projector.
Repository: localaiLicense: mit
thinkingcap-qwen3.8-27b
ThinkingCap is a 27B Qwen3.8 fine-tune trained to reduce reasoning tokens, with text and image input.
This Q4_K_M GGUF build uses llama.cpp, the embedded chat template, and the F16 vision projector.
Licensed under PolyForm Small Business 1.0.0 with the publisher's personal-use grant; see the model license for permitted use.
Repository: localaiLicense: polyform-small-business-1.0.0
thinkingcap-qwen3.8-27b-q8
ThinkingCap is a 27B Qwen3.8 fine-tune trained to reduce reasoning tokens, with text and image input.
This Q8_0 GGUF build uses llama.cpp, the embedded chat template, and the F16 vision projector.
Licensed under PolyForm Small Business 1.0.0 with the publisher's personal-use grant; see the model license for permitted use.
Repository: localaiLicense: polyform-small-business-1.0.0
qwen3.8-27b-uncensored-hauhaucs-aggressive-mtp
# Qwen3.8-27B
> [!Note]
> This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.
>
> These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, TokenSpeed, etc.
> [!Tip]
> For users seeking managed, scalable inference without infrastructure maintenance, the official Qwen API service is provided by Qwen Cloud.
> In particular, **Qwen3.8-27B** will be available as a hosted version with more production features, e.g., 1M context length by default, official built-in tools. For more information, please refer to the Qwen3.8-27B Overview. The service is coming soon. Stay tuned for updates.
Following the widespread community adoption of the Qwen3.5 and Qwen3.6 series, we are pleased to introduce Qwen3.8, the most capable generation in the Qwen open-model family to date.
...
Repository: localaiLicense: apache-2.0
apodex-1.1-mini-q4
Apodex-1.1-mini is an Apache-2.0 Qwen3.5 mixture-of-experts model for
long-horizon research, data analysis, coding, file work, and tool use. It
activates about 3B of its 35.95B parameters per token and supports text and
image input with a context window of 262K tokens.
This default entry uses the recommended Q4_K_M GGUF and F16 vision
projector. An MTP-enabled build and a higher-quality Q8_0 model are
available as variants.
Repository: localaiLicense: apache-2.0
apodex-1.1-mini-q4-mtp
Apodex-1.1-mini with MTP speculative decoding enabled on the recommended
Q4_K_M GGUF. The model carries its native MTP head, so it needs no separate
draft model. The F16 vision projector supports multimodal prompts.
Repository: localaiLicense: apache-2.0
apodex-1.1-mini-q8
Apodex-1.1-mini in the higher-quality Q8_0 GGUF format, with the shared F16
vision projector for multimodal prompts.
Repository: localaiLicense: apache-2.0
glm-5.3-flash-q4
GLM-5.3-Flash is Z.ai's natively multimodal 320B-parameter mixture-of-experts
model with 18B active parameters. It combines sparse and linear attention
for coding, agentic work, tool use, vision, and long-context tasks. This
entry uses the UD-Q4_K_XL GGUF quantization and enables the model's MTP
speculative-decoding head.
Repository: localaiLicense: mit
glm-5.3-flash-q8
GLM-5.3-Flash is Z.ai's natively multimodal 320B-parameter mixture-of-experts
model with 18B active parameters. It combines sparse and linear attention
for coding, agentic work, tool use, vision, and long-context tasks. This
entry uses the higher-quality Q8_0 GGUF quantization and enables the model's
MTP speculative-decoding head.
Repository: localaiLicense: mit
huihui-qwen3.8-flash-next-abliterated-q4
Huihui's abliterated Qwen3.8-Flash-Next is a vision-language
mixture-of-experts model modified to reduce refusals. This entry uses
the publisher's UD-Q4_K_XL GGUF and BF16 vision projector for text chat
and image input through llama.cpp. The default context is 32,768 tokens.
Model weights use the Qwen Community License 1.0.
Repository: localaiLicense: other
qwen3.8-flash-next-q4
Qwen3.8-Flash-Next is Qwen's 125B-parameter, 6B-active experimental
vision-language mixture-of-experts model. It targets agentic coding,
reasoning, tool use, and long-context workloads with a native 262K-token
context window.
This default entry uses Unsloth's UD-Q4_K_XL GGUF and BF16 vision
projector. Linked variants offer Q8_0 and AtomicChat's smaller IQ4_XS
and Q4_K_M builds with a separate n-gram table shard.
Repository: localaiLicense: other
qwen3.8-flash-next-q8
Qwen3.8-Flash-Next in the higher-quality Q8_0 GGUF format, with the shared
BF16 vision projector. This build preserves more model quality but needs
more memory than the default Q4 variant.
Repository: localaiLicense: other
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