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AI 모델 2119
- H3-Turbo-Neat↗
moe-kill
A practical family of low-step MiniMax H3 LoRAs for both video and still-image generation, focused on image quality, motion, acting, prompt stability, audio, and real-world usability. > **Important:** Neat Classic and Neat Prime use different workflows and sampling setups. > Do not mix their sampler nodes or recommended settings. 它最大的实用价值之一，是可以让 **H3 自己为 H3 制作参考图**：先在相同模型审美空间中完成角色、服装、灯光、场景与镜头设计，再将参考图直接回喂给 H3 做视频。相比使用外部生图模型，这种同源参考图与 H3 的色彩、人物塑形和材质逻辑天然一致，能够显著减少风格不匹配和参考图“进视频后变味”的问题。
- MiniCPM5-1B-Base↗
openbmb
MiniCPM Tech Report | MiniCPM Wiki(Chinese) | GitHub Repo | UltraData | MiniCPM Desk Pet | Online Demo We are releasing **MiniCPM5-1B**, the first model in the **MiniCPM5** series. It is a dense 1B Transformer built for on-device, local deployment, and resource-constrained scenarios, reaching 1B-class open-source SOTA. 🏆 **1B-class open-source SOTA**: compared with strong open-source models in the same size class, MiniCPM5-1B reaches SOTA within this comparison set. Its advantage is most visible in agentic tool use, code generation, and difficult reasoning.
- resnet101.a1h_in1k↗
timm
This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample Recipe details: * Based on ResNet Strikes Back `A1` recipe * LAMB optimizer * Stronger dropout, stochastic depth, and RandAugment than paper `A1` recipe * Cosine LR schedule with warmup
- FLUX.2-klein-4B-FP8↗
unsloth
This is an FP8 / INT8 quantized version of FLUX.2-klein-4B. - Optimized for efficient inference with reduced memory footprint. Same-seed LPIPS vs the bf16 model (lower is better): 0.123 INT8, 0.104 FP8. The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in as low as under a second**. Built for applications that require real-time image generation without sacrificing quality, and runs on consumer hardware, with as little as 13GB VRAM. FLUX.2 [klein] 4B is a 4 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.
- asr-wav2vec2-ctc-french↗
bofenghuang
This model is a fine-tuned version of LeBenchmark/wav2vec2-FR-7K-large, trained on a composite dataset comprising of over 2200 hours of French speech audio, using the train and validation splits of Common Voice 11.0, Multilingual LibriSpeech, Voxpopuli, Multilingual TEDx, MediaSpeech, and African Accented French. When using the model make sure that your speech input is also sampled at 16Khz.
- Cosmos-UpsamplePrompt1-12B-Transfer↗
NVIDIA
- sarashina2.2-vision-3b-gguf↗
samuraieng
- FastSAM-ExecuTorch↗
software-mansion
- samantha-1.2-mistral-7B-GGUF↗
TheBloke
- FLUX.1-dev-GGUF↗
unsloth
`FLUX.1 [dev]` is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post. 1. Cutting-edge output quality, second only to our state-of-the-art model `FLUX.1 [pro]`. 2. Competitive prompt following, matching the performance of closed source alternatives . 3. Trained using guidance distillation, making `FLUX.1 [dev]` more efficient. 4. Open weights to drive new scientific research, and empower artists to develop innovative workflows. 5. Generated outputs can be used for personal, scientific, and commercial purposes as described in the [`FLUX.1 [dev]` Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). We provide a reference implementation of `FLUX.1 [dev]`, as well as sampling code, in a dedicated github repository. Developers and creatives looking to build on top of `FLUX.1 [dev]` are encouraged to use this as a starting point. The FLUX.1 models are also available via API from the following sources - bfl.ml (currently `FLUX.1 [pro]`) - replicate.com - fal.ai - mystic.ai `FLUX.1 [dev]` is also available in Comfy UI for local inference with a node-based workflow. To use `FLUX.1 [dev]` with the 🧨 diffusers python library, first install or upgrade diffusers To learn more check out the diffusers documentation --- - This model is not intended or able to provide factual information. - As a statistical model this checkpoint might amplify existing societal biases. - The model may fail to generate output that matches the prompts. - Prompt following is heavily influenced by the prompting-style.