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AI 模型 2119
- wav2vec2-large-xlsr-53-hebrew↗
imvladikon
Fine-tuned facebook/wav2vec2-large-xlsr-53 on the several downloaded youtube samples. When using this model, make sure that your speech input is sampled at 16kHz. The model can be used directly (without a language model) as follows:
- Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-NVFP4-GGUF↗
jan1k
NVFP4 quantisations of HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF, built with michaelw9999/advanced-gguf-quantizer (a llama.cpp fork focused on NVFP4/MXFP6 quantization). All six share the same source, protection policy, and architecture profile (`qwen35dense`). They differ only in the calibration data used to compute NVFP4 input scales (or, for v2, the absence of it). Requantised from Q8_K_P, not BF16. Tensors that were q8 in the source go through one extra rounding step; bf16 source tensors requantise cleanly.
- all-mpnet-base-v2↗
sentence-transformers
这是一个 sentence-transformers 模型：它将句子和段落映射到一个 768 维的稠密向量空间，可用于聚类或语义搜索等任务。如果没有 sentence-transformers，您也可以这样使用该模型：首先，将输入通过 Transformer 模型，然后需要在上下文相关的词嵌入之上应用适当的池化操作。该项目旨在利用自监督对比学习目标，在超大规模的句子级别数据集上训练句子嵌入模型。我们使用了预训练的 `microsoft/mpnet-base` 模型，并在包含 10 亿对句子的数据集上进行了微调。我们采用对比学习目标：给定一对句子中的一个句子，模型应预测在一组随机采样的其他句子中，哪一个实际上与之在我们的数据集中配对。
- Yi-9B↗
01-ai
If you're interested in Yi's adoption of Llama architecture and license usage policy, see Yi's relation with Llama. ⬇️ > 💡 TL;DR > > The Yi series models adopt the same model architecture as Llama but are **NOT** derivatives of Llama. 🔥 2024-07-29 : The Yi Cookbook 1.0 is released, featuring tutorials and examples in both Chinese and English.
- distilcamembert-base-ner↗
cmarkea
We present DistilCamemBERT-NER, which is DistilCamemBERT fine-tuned for the NER (Named Entity Recognition) task for the French language. The work is inspired by Jean-Baptiste/camembert-ner based on the CamemBERT model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase, for example. Indeed, inference cost can be a technological issue. To counteract this effect, we propose this modelization which **divides the inference time by two** with the same consumption power thanks to DistilCamemBERT. This model performance is compared to 2 reference models (see below) with the metric f1 score. For the mean inference time measure, an AMD Ryzen 5 4500U @ 2.3GHz with 6 cores was used:
- encodec_24khz↗
facebook
This model card provides details and information about EnCodec, a state-of-the-art real-time audio codec developed by Meta AI. EnCodec is a high-fidelity audio codec leveraging neural networks. It introduces a streaming encoder-decoder architecture with quantized latent space, trained in an end-to-end fashion. The model simplifies and speeds up training using a single multiscale spectrogram adversary that efficiently reduces artifacts and produces high-quality samples. It also includes a novel loss balancer mechanism that stabilizes training by decoupling the choice of hyperparameters from the typical scale of the loss. Additionally, lightweight Transformer models are used to further compress the obtained representation while maintaining real-time performance. - **Repository:** GitHub Repository - **Paper:** EnCodec: End-to-End Neural Audio Codec
- jebadiah-9b-v2-GGUF↗
frontier-infra
> **Just want to run it?** Download jebadiah-9b-v2-1-GGUF and open it in llama.cpp, Ollama or LM Studio. > > **Which one should I use?** Start with the 9B. It's smaller and faster, and it's what most people run. Pick the 27B if you have the memory and want the higher score (Decision Index Full 55.94 against the 9B's 44.91, board data of 2026-10-15). On Apple silicon, use the MLX builds of the same models. You can try the 4B in the browser first: jebadiah-playground. > > This repo: the 9B v2 GGUF quants (Q4_K_M, Q5_K_M, Q8_0), for llama.cpp, Ollama and LM Studio. **Jeb is a decision model, not a chat model.** Use `jeb serve` or the decide scripts (see Run Jeb locally on jebadiah.ai). In an LM Studio or Ollama chat window it will look wrong. Trying it in a chat window? Use the optional chat profiles: https://github.com/getainode/jebadiah/blob/main/docs/chat-window.md
- wolf-defender-prompt-injection↗
patronus-studio
Explore the integrations above, or see how Personaxis, Wanaku and Mygentic Cello integrate Wolf Defender. Wolf Defender is an open, multilingual classifier that detects prompt injection and jailbreak-style instructions before they reach your LLM. Use it to screen user inputs, retrieved documents, emails, web pages and agent tool outputs, with inference running on your own hardware. All models below were evaluated with the same threshold (`0.5`) and document-scoring protocol: 2,048-token windows, 64-token overlap and normalized Smooth-Max aggregation.
- KR-FinBert-SC↗
snunlp
Much progress has been made in the NLP (Natural Language Processing) field, with numerous studies showing that domain adaptation using small-scale corpus and fine-tuning with labeled data is effective for overall performance improvement. we proposed KR-FinBert for the financial domain by further pre-training it on a financial corpus and fine-tuning it for sentiment analysis. As many studies have shown, the performance improvement through adaptation and conducting the downstream task was also clear in this experiment. The training data for this model is expanded from those of **KR-BERT-MEDIUM**, texts from Korean Wikipedia, general news articles, legal texts crawled from the National Law Information Center and Korean Comments dataset. For the transfer learning, **corporate related economic news articles from 72 media sources** such as the Financial Times, The Korean Economy Daily, etc and **analyst reports from 16 securities companies** such as Kiwoom Securities, Samsung Securities, etc are added. Included in the dataset is 440,067 news titles with their content and 11,237 analyst reports. **The total data size is about 13.22GB.** For mlm training, we split the data line by line and **the total no. of lines is 6,379,315.** KR-FinBert is trained for 5.5M steps with the maxlen of 512, training batch size of 32, and learning rate of 5e-5, taking 67.48 hours to train the model using NVIDIA TITAN XP.
- Dr.Samantha-8B-GGUF↗
mradermacher