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AI 모델 2119
- 01-ai_-_Yi-1.5-34B-gguf↗
RichardErkhov
Yi-1.5-34B - GGUF - Model creator: https://huggingface.co/01-ai/ - Original model: https://huggingface.co/01-ai/Yi-1.5-34B/ Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
- OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF↗
TheBloke
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
- eca_nfnet_l0.ra2_in1k↗
timm
A ECA-NFNet-Lite (Lightweight NFNet w/ ECA attention) image classification model. Trained in `timm` by Ross Wightman. Normalization Free Networks are (pre-activation) ResNet-like models without any normalization layers. Instead of Batch Normalization or alternatives, they use Scaled Weight Standardization and specifically placed scalar gains in residual path and at non-linearities based on signal propagation analysis. Lightweight NFNets are `timm` specific variants that reduce the SE and bottleneck ratio from 0.5 -> 0.25 (reducing widths) and use a smaller group size while maintaining the same depth. SiLU activations used instead of GELU.
- flux-3-image-klein-comfy↗
ml-resources
> Ungated mirror of black-forest-labs/flux-3-image-klein-comfy. All credit to Black Forest Labs. This is not an official Black Forest Labs release. The model is licensed under the FLUX Kommunity License (non-commercial), included here as `LICENSE.md` with `NOTICE.md`; any rights to use it are granted directly by Black Forest Labs under that license. FLUX 3 Image Klein is a 5.6B open weights model for text-to-image generation and image editing. It takes a prompt and up to 10 reference images and returns one image. Text-to-image, single-reference edits and multi-reference compositions run through the same model, with Qwen3-VL-4B-Instruct as the text encoder. The 5.6B parameter label refers to the image transformer; the text encoder is separate.
- topsy_turvy_human_v1↗
samsl
This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Library: [More Information Needed] - Docs: [More Information Needed]
- Qwen-Edit-2509-Upscale-LoRA↗
vafipas663
**V4 Candidates** LoRA A: qwen-edit-enhance-v4-16_a_000003000/1000, LoRA B: qwen-edit-enhance-v4-64_a_000003750/4750 Prompt: Enhance image quality, followed by textual description of the scene. The more descriptive it is, the better the upscale effect will be All images below are from 8 step Lighting LoRA. * ModelSamplingAuraFlow is a must, shift must be kept **below** 0.3. With higher resolutions, such as image 3, you can set it as low as 0.02 * Samplers - LCM (best), Euler_Ancestral, then Euler * Schedulers all work and give varying results in terms of smoothness
- LLaVA-NeXT-Video-7B-hf↗
llava-hf
Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1CZggLHrjxMReG-FNOmqSOdi4z7NPq6SO?usp=sharing) Disclaimer: The team releasing LLaVa-NeXT-Video did not write a model card for this model so this model card has been written by the Hugging Face team. **Model type:** LLaVA-Next-Video is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. The model is buit on top of LLaVa-NeXT by tuning on a mix of video and image data to achieve better video understanding capabilities. The videos were sampled uniformly to be 32 frames per clip. The model is a current SOTA among open-source models on VideoMME bench. Base LLM: lmsys/vicuna-7b-v1.5
- LTXV-13B-0.9.8-distilled-GGUF↗
QuantStack
The model files can be used in ComfyUI with the ComfyUI-GGUF custom node. Place the required model(s) in the following folders: The old example workflow from last version should still work, youll just need to switch the vae and the gguf. **Example workflow** - based on the official example workflow *As this is a quantized model not a finetune, all the same restrictions/original license terms still apply.*
- Srijith-rkr_-_deepseek_base_1e-4_NO_cot_only_failed_samples_300_ckpt-gguf↗
RichardErkhov
- Qwen3.8-27B-nvfp4full-NInfer↗
cometkim
The repository contains a fuller-NVFP4 weight profile of Qwen/Qwen3.8-27B in the native NInfer `.ninfer` artifact format, with the z-lab/Qwen3.8-27B-DFlash2 block-diffusion speculative drafter embedded in the same image. It runs on NInfer **v3** engines — Text and MTP components through format/shape routes the upstream runtime already implements, the optional NVFP4-encoded DFlash2 companion through the fork's runtime support. It is not a Transformers checkpoint, Safetensors distribution, or GGUF file. Compared with the official `nvfp4` profile it extends NVFP4 from the layers 0–55 MLP to nearly the whole Text backbone: - every Text `mlp/gate_up` and `mlp/down` is NVFP4 (the layers 0–55 words are copied bit-exactly from unsloth/Qwen3.8-27B-NVFP4; layers 56–63 are quantized locally from the official BF16 checkpoint); - every GDN `query_key_value_z` is NVFP4, and GDN `output` is NVFP4 on 47 of 48 layers; - full-attention `query_key_gate_value` is NVFP4 on the ten deepest layers and `attention/output` on 14/16, with 9 BF16 exception parents retaining the registered Qwen3.6-27B NVFP4 exception pattern; - the token embedding and full output head use groupwise W8G32_F16S instead of row-scaled FP8. This yields 247 NVFP4 parents with 247 site-level input divisors. Locally quantized parents use the documented encoder profile `NVFP4_MAXABS_DIVISOR_RNE_V1` (per-tensor FP32 divisor, per-16 E4M3FN block scales, RNE E2M1 codes) with site divisors calibrated by streaming the official BF16 checkpoint. The complete contract, encoder, calibration corpus, and validation evidence are `docs/maintainer/qwen3.8-27b-artifact.md` §14 in the fork.