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AI 모델 2119
- wav2vec2-large-xlsr-53-german↗
jonatasgrosman
Fine-tuned facebook/wav2vec2-large-xlsr-53 on German using the train and validation splits of Common Voice 6.1. 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
- Sage-A1.5-Heretic-Abliterated-Uncensored-i1-GGUF↗
mradermacher
weighted/imatrix quants of https://huggingface.co/sam33339999/Sage-A1.5-Heretic-Abliterated-Uncensored static quants are available at https://huggingface.co/mradermacher/Sage-A1.5-Heretic-Abliterated-Uncensored-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
- speechless-mistral-dolphin-orca-platypus-samantha-7B-GGUF↗
TheBloke
- gemma-4-26B-A4B-it-assistant↗
Google
> [!Note] > This model card is for the Multi-Token Prediction (MTP) drafters for the Gemma 4 models. MTP is implemented by extending the base model with a smaller, faster draft model. When used in a Speculative Decoding pipeline, the draft model predicts several tokens ahead, which the target model then verifies in parallel. This results in significant decoding speedups (up to 3x) while guaranteeing the exact same quality as standard generation, making these checkpoints perfect for low-latency and on-device applications. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: **E2B**, **E4B**, **12B**, **26B A4B**, and **31B**. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.
- CodeBooga-34B-v0.1↗
oobabooga
It was created with the BlockMerge Gradient script, the same one that was used to create MythoMax-L2-13b, and with the same settings. The following YAML was used: I made a quick experiment where I asked a set of 3 Python and 3 Javascript questions (real world, difficult questions with nuance) to the following models: 1) This one 2) A second variant generated with `model_path1` and `model_path2` swapped in the YAML above, which I called CodeBooga-Reversed-34B-v0.1 3) WizardCoder-Python-34B-V1.0 4) Phind-CodeLlama-34B-v2
- sam-vit-base↗
Xenova
- ltx-2.3-mlx-q8↗
dgrauet
Int8 quantization (group_size 64, transformer block Linear weights only) of dgrauet/ltx-2.3-mlx, the MLX conversion of Lightricks/LTX-2.3. These weights are a derivative work and are distributed under the same terms as the model they were converted from. A verbatim copy ships with them as `LICENSE`, and the upstream text is at https://github.com/Lightricks/LTX-2/blob/main/LICENSE-2. Use of these weights is subject to it, including any restrictions it attaches. These weights can be used with ltx-2-mlx, a native MLX inference pipeline for LTX-2.3 on Apple Silicon:
- wav2vec2-base-superb-ks↗
superb
The base model is wav2vec2-base, which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. SUPERB uses the widely used Speech Commands dataset v1.0 for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the false positive. For the original model's training and evaluation instructions refer to the S3PRL downstream task README.
- Llama-2-Coder-7B-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 `apache-2.0`, 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.
- Scion-FlashNext-176B-A6B↗
SkyIsNotGreen
A full Qwen3.8-Flash-Next in one 57.62 GB GGUF for llama.cpp — **176B parameters in the file**: the 125B language model (6B activated) plus the 51.2B-parameter n-gram table (the base's 4B MTP head is not included). Ternary expert banks plus small trained corrections, and the n-gram table (PLE) in the per-row **Q4X160** container (26.24 GB) — no full-precision masters, no full-model QAT. Two larger containers ship in the same repo: the Q4_0-table file at 60.17 GB and the F16-body file at 65.80 GB. > **2.60 bpw** | **57.62 GB** (3.2x smaller than the FP8 release) | **smallest build of this model published on the Hub that we can see, pruned or not** | **cap_eval 15/20, 0 harness errors** | **HellaSwag 400 82.00%, Winogrande 400 77.50%** The name comes from grafting. A scion is the shoot grafted onto a rootstock, and here the trained corrections are grafted onto a 1.75 bpw ternary expert body.