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AI モデル 2119
- KAT-Coder-V2.5-Dev-MTP-GGUF↗
gbuzhf
KAT ships **`mtp_num_hidden_layers: 0`** — no draft head. These builds graft Qwen3.6-35B-A3B's **original** MTP head onto KAT's trunk, quantized with an imatrix calibrated on KAT's own output. Includes the **bf16 master** so you can build any tier yourself without a 69 GB safetensors pull or a conversion. We fine-tuned this head twice on KAT's own rollouts. **Both fine-tunes made it worse.** Measured live on 79 configs, same tier, same flags, only the head differing:
- indobert-base-p1↗
indobenchmark
IndoBERT was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti.
- stable-diffusion-inpainting↗
stable-diffusion-v1-5
Stable Diffusion Inpainting is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask. The **Stable-Diffusion-Inpainting** was initialized with the weights of the Stable-Diffusion-v-1-2. First 595k steps regular training, then 440k steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning to improve classifier-free classifier-free guidance sampling. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. You can use this both with the 🧨Diffusers library and RunwayML GitHub repository ( now deprecated ), Automatic1111 .
- KVAE-3D-2.0-t4s16↗
kandinskylab
KVAE-Video 2.0 t4s16 is the higher-compression 4 x 16 x 16 video tokenizer in the KVAE 2.0 family. It is designed to provide the latent space for diffusion models, encoding videos into continuous 64-channel representations and reconstructing them with high fidelity. Its causal, fully convolutional architecture supports long videos through segment-wise processing with temporal caching. Run from the KVAE source repository root. The reference environment uses Python 3.11, PyTorch 2.8.0, and CUDA 12.8. Temporal segments are processed through internal block caches. Do not interleave independent videos on the same model instance; use one `KVAEVideo` instance per concurrent stream.
- llama-3-8b-samantha-i1-GGUF↗
mradermacher
- Flint-40B-A2.5B-abliterated-GGUF↗
dima6312
An abliterated (uncensored) Flint: RecursiveSI's Flint 40B-A2.5B with its refusal direction removed, for security research and coding work: exploit analysis, malware triage, detection engineering, CTFs and red-team tooling that the original model often declines. **Summary:** held-out MITRE ATT&CK refusals 15% → 0% (n=100, Q8_0). No significant drop on HumanEval+, MBPP+, GSM8K, MMLU-Pro, CyberMetric or WMDP-cyber (paired, same items). KL divergence from the original on harmless prompts: 0.0146. Builds: BF16, GGUF (Q8_0 to Q3_K_M), NVFP4 for NVIDIA Blackwell, MLX 4-bit for Apple silicon. **Not sure?** Take the GGUF `Q4_K_M` (24 GB). It runs in llama.cpp and in apps built on it, including LM Studio (its llama.cpp runtime 2.63.0 includes Flint). The MLX builds are for Mac users who already run mlx-lm.
- Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF↗
hesamation
GGUF quantizations of `hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled`, a reasoning SFT fine-tune of `Qwen/Qwen3.6-35B-A3B` on Claude Opus 4.6-style chain-of-thought distillation data. The source fine-tune is text-only. The Qwen3.6 base architecture includes a vision encoder, but this fine-tuning run did not train on image or video examples. Treat these GGUF files as text-generation/runtime quantizations of the merged fine-tuned checkpoint. - **Developed by:** @hesamation - **Source model:** `hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled` - **Base model:** `Qwen/Qwen3.6-35B-A3B` - **License:** apache-2.0
- indobert-large-p2↗
indobenchmark
IndoBERT is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective. IndoBERT was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti.
- MERT-v1-330M↗
m-a-p
The development log of our Music Audio Pre-training (m-a-p) model family: - 02/06/2023: arxiv pre-print and training codes released. - 17/03/2023: we release two advanced music understanding models, MERT-v1-95M and MERT-v1-330M , trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. - 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset MERT-v0-public - 29/12/2022: a music understanding model MERT-v0 trained with **MLM** paradigm, which performs better at downstream tasks. - 29/10/2022: a pre-trained MIR model music2vec trained with **BYOL** paradigm. The m-a-p models share the similar model architecture and the most distinguished difference is the paradigm in used pre-training. Other than that, there are several nuance technical configuration needs to know before using: - **Model Size**: the number of parameters that would be loaded to memory. Please select the appropriate size fitting your hardware. - **Transformer Layer-Dimension**: The number of transformer layers and the corresponding feature dimensions can be outputted from our model. This is marked out because features extracted by **different layers could have various performance depending on tasks**. - **Feature Rate**: Given a 1-second audio input, the number of features output by the model. - **Sample Rate**: The frequency of audio that the model is trained with.
- ERNIE-Image-Turbo-GGUF↗
unsloth
This is a GGUF quantized version of ERNIE-Image-Turbo. unsloth/ERNIE-Image-Turbo-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision. - Uses tooling from ComfyUI-GGUF by city96. ERNIE-Image-Turbo is an open text-to-image generation model developed by the ERNIE-Image team at Baidu. It is the distilled release of ERNIE-Image, built on the same single-stream Diffusion Transformer (DiT) family and designed for fast generation with strong fidelity in only 8 inference steps. The model retains strong controllability in practical generation scenarios where accurate content realization matters as much as aesthetics. In particular, ERNIE-Image-Turbo remains strong on complex instruction following, text rendering, and structured image generation, making it well suited for posters, comics, multi-panel layouts, and other content creation tasks that require both visual quality and efficiency. It also supports a broad range of visual styles, including realistic photography, design-oriented imagery, and stylized aesthetic outputs. **Highlights:** - **Fast and efficient**: As the distilled checkpoint of ERNIE-Image, ERNIE-Image-Turbo delivers strong generation quality with only 8 inference steps, making it suitable for latency-sensitive applications. - **Text rendering**: ERNIE-Image-Turbo performs well on dense, long-form, and layout-sensitive text, making it a strong choice for posters, infographics, UI-like images, and other text-heavy visual content. - **Instruction following**: The model is able to follow complex prompts involving multiple objects, detailed relationships, and knowledge-intensive descriptions with strong reliability. - **Structured generation**: ERNIE-Image-Turbo is effective for structured visual tasks such as posters, comics, storyboards, and multi-panel compositions, where layout and organization are critical. - **Style coverage**: In addition to clean and readable design-oriented outputs, the model also supports realistic photography and distinctive stylized aesthetics, including softer and more cinematic visual tones. - **Practical deployment**: Thanks to its compact size, ERNIE-Image-Turbo