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AI 模型 2119
- Ornith-1.5-35B-A3B-Abliterated-CyberTiel-Calibrated-MTPv2-ICE-GGUF↗
gbuzhf
Four ICE tiers of **huihui-ai/Huihui-Ornith-1.5-35B-A3B-abliterated**, quantized against a **trunk-matched** importance matrix, with the model's own trained **MTPv2** head and the **Qwen-Sharp v22.4.1** chat template embedded. The BF16 master they were all built from is published here too. > ⚠️ **Uncensored model.** This is an abliterated checkpoint: refusal behaviour has been > suppressed in the weights. It will comply with requests a stock model declines. You are > responsible for how you use it. Sandbox it at the OS level and control its network and > code-execution access — with no refusal backstop, a prompt injection from a hostile page > or third-party code has nothing to stop it. If you want the guardrailed equivalent, use > the non-abliterated TIEL-Calibrated ICE tiers. Rule of thumb: take the largest tier that leaves you the context you actually need at `q8_0` KV. If you already run a `UD` build of this model, pick the ICE tier at the same size and keep everything else the same.
- MiniMax-H3-PTD-LoRAs↗
taobao-content-ai
We apply **PTD (Parametric Trajectory Distillation)** to MiniMax-H3, distilling the Ref2VA (reference-image → video + audio) trajectory into a **few-step** student. We release an **8-step** and a **4-step** accelerator for the Ref2VA task. > [!TIP] > **Want better quality or lower latency?** Check out `MiniMax-H3-PTD-RefAsymOPD-LoRAs` — the upgraded sibling trained with **PTD + RefAsymOPD** (Reference-Asymmetric On-Policy Distillation). > It ships an **8-step** and a new **6-step** variant, both delivering noticeably better **background plausibility, sharpness and realism** — surpassing the plain PTD checkpoints in this repo as well as the base model at 40 NFE. > Same checkpoint format and the same `apply_ptd_lora` call — a one-line filename swap. Unlike a single shared acceleration LoRA, PTD trains **one independent low-rank adapter per inference step** ("per-hop" LoRA) on the diffusion backbone, on top of the per-interval output heads introduced by Parallel Decoding Distillation (PDD). At inference, each denoising step selects its own hop's adapter and fuses that step's output head, so an 8-hop checkpoint runs in **8** transformer evaluations (NFE) and a 4-hop checkpoint in **4**.
- Qwen3.6-35B-A3B-MTP-GGUF↗
byteshape
This is a GGUF-quantized version of Qwen3.6-35B-A3B with **Multi-Token Prediction (MTP)** support, produced with **ByteShape's ShapeLearn**, which learns the optimal datatype per tensor to maintain high quality even at very low bitlengths. MTP allows the model to predict multiple tokens per forward pass, accelerating decoding on GPUs by accepting speculative tokens that match what the base model would have produced. These models are optimized for **GPU-only** inference. Pick a model from the table below and click **Get llama.cpp command** to get a ready-to-run command with all the correct sampling parameters for this model.
- mms-tts-khm↗
facebook
This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to synthesise speech with different rhythms from the same input text.
- HY-MT1.5-7B-GGUF↗
腾讯
🤗 Hugging Face | 🕹️ Demo 🤖 ModelScope | Hunyuan Translation Model Version 1.5 includes a 1.8B translation model, HY-MT1.5-1.8B, and a 7B translation model, HY-MT1.5-7B. Both models focus on supporting mutual translation across 33 languages and incorporating 5 ethnic and dialect variations. Among them, HY-MT1.5-7B is an upgraded version of our WMT25 championship model, optimized for explanatory translation and mixed-language scenarios, with newly added support for terminology intervention, contextual translation, and formatted translation. Despite having less than one-third the parameters of HY-MT1.5-7B, HY-MT1.5-1.8B delivers translation performance comparable to its larger counterpart, achieving both high speed and high quality. After quantization, the 1.8B model can be deployed on edge devices and support real-time translation scenarios, making it widely applicable. - HY-MT1.5-1.8B achieves the industry-leading performance among models of the same size, surpassing most commercial translation APIs. - HY-MT1.5-1.8B supports deployment on edge devices and real-time translation scenarios, offering broad applicability. - HY-MT1.5-7B, compared to its September open-source version, has been optimized for annotated and mixed-language scenarios. - Both models support terminology intervention, contextual translation, and formatted translation.
- wav2vec2-large-xlsr-53-hungarian↗
jonatasgrosman
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hungarian using the train and validation splits of Common Voice 6.1 and CSS10. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
- ThinkingCap-Qwen3.8-27B-AutoRound-W4A16↗
wasifb
An INT4 AutoRound quantization of bottlecapai/ThinkingCap-Qwen3.8-27B, BottleCap AI's reasoning fine-tune of Qwen/Qwen3.8-27B. It is **19.0 GB** and keeps the **MTP head** for self-speculative decoding. BottleCap publishes FP8, NVFP4 and GGUF builds, but no INT4 AutoRound. This one exists so the model can run on two 24 GB cards with the same tiers as the base Qwen3.8-27B in club-3090. The packed layout is tensor-for-tensor identical to Frozenlock/Qwen3.8-27B-int4-AutoRound: the same quantized and BF16 module families, and the same MTP tensors. So it should load anywhere that checkpoint does. Only the tuning differs: 512 samples with alg_ext here, where that card says "default tuning".
- qwen3-asr-0.6b-GGUF↗
cstr
GGUF / ggml conversions of `Qwen/Qwen3-ASR-0.6B` for use with the `qwen3-asr-main` CLI from **CrispStrobe/CrispASR**. This is the **first speech-LLM** in the CrispASR family — every other model in the set uses a dedicated CTC / transducer / encoder-decoder. The Qwen3-ASR runtime ships with a persistent KV cache so per-token decode is O(1) in cache size, not O(N) full re-forwards. All quantisations produce the correct transcript on `samples/jfk.wav`: > And so, my fellow Americans, ask not what your country can do for you; ask what you can do for your country.
- FLUX.1-dev↗
frankjoshua
`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.
- wav2vec2-large-xlsr-georgian↗
xsway
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Georgian using the Common Voice. When using this model, make sure that your speech input is sampled at 16kHz.