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AI 모델 449
- M2V_base_output↗
minishlab
This Model2Vec model is a distilled version of the baai/bge-base-en-v1.5 Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec. It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using zipf weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence.
- wav2vec2-large-robust↗
facebook
The large model pretrained on 16kHz sampled speech audio. Speech datasets from multiple domains were used to pretrain the model: - Libri-Light: open-source audio books from the LibriVox project; clean, read-out audio data - CommonVoice: crowd-source collected audio data; read-out text snippets - Switchboard: telephone speech corpus; noisy telephone data - Fisher: conversational telephone speech; noisy telephone data **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. Authors: Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski, Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Jacob Kahn, Ann Lee, Ronan Collobert, Gabriel Synnaeve, Michael Auli
- Mistral-Nemo-Instruct-2407-abliterated-GGUF↗
QuantFactory
This is quantized version of natong19/Mistral-Nemo-Instruct-2407-abliterated created using llama.cpp Abliterated version of Mistral-Nemo-Instruct-2407, a Large Language Model (LLM) trained jointly by Mistral AI and NVIDIA that significantly outperforms existing models smaller or similar in size. The model's strongest refusal directions have been ablated via weight orthogonalization, but the model may still refuse your request, misunderstand your intent, or provide unsolicited advice regarding ethics or safety.
- gpt2-xl↗
openai-community
- Model Details - How To Get Started With the Model - Uses - Risks, Limitations and Biases - Training - Evaluation - Environmental Impact - Technical Specifications - Citation Information - Model Card Authors **Model Description:** GPT-2 XL is the **1.5B parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. - **Developed by:** OpenAI, see associated research paper and GitHub repo for model developers. - **Model Type:** Transformer-based language model - **Language(s):** English - **License:** Modified MIT License - **Related Models:** GPT-2, GPT-Medium and GPT-Large - **Resources for more information:** - Research Paper - OpenAI Blog Post - GitHub Repo - OpenAI Model Card for GPT-2 - OpenAI GPT-2 1.5B Release Blog Post - Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
- bark-small↗
suno
Bark is a transformer-based text-to-audio model created by Suno. Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. The model can also produce nonverbal communications like laughing, sighing and crying. To support the research community, we are providing access to pretrained model checkpoints ready for inference. This model is meant for research purposes only. The model output is not censored and the authors do not endorse the opinions in the generated content. Use at your own risk. 2. Run inference via the `Text-to-Speech` (TTS) pipeline. You can infer the bark model via the TTS pipeline in just a few lines of code!
- Rocinante-X-12B-v1-GGUF↗
TheDrummer
Hi! I'm Drummer. I'm a Software Engineer with experience in JavaScript, Golang, Python, and generally engineering the crap out of things. - **Exploration:** Everyone is trying to figure out how AI works and what it's capable of. I am too - just not in creating the smartest, safest model at all costs. - **Upskill:** The world is headed towards AI. It is here to stay. This has been my way of brushing up in this new form of computing challenge. - **Value:** I yearn to create value. I feel satisfaction and fulfillment in providing something meaningful for others. - **Fun:** It's just fun using and making models. It's also fun coming up with theories and realizing them in practice (training AI). I started my tuning venture back in mid-2024 when I wanted to improve its literary capabilities. I've come a long way since then and I have branched out and specialized. Foundational models today are optimized for non-creative uses, and I believe there is a place for AI in creativity and entertainment.
- Huihui-Qwen3.8-27B-abliterated-GGUF↗
Weirdo333
This is an uncensored version of Qwen/Qwen3.8-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. The newly added **Huihui-Qwen3.8-27B-abliterated-Underdog-Saluki** series come from ConwayResearch/Underdog-Saluki-27B-1.0 and ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF. Only layers **22 to 52 (0-based indexing)** have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. Due to ConwayResearch/Underdog-Saluki-27B-1.0 not providing an imatrix file, we will attempt to perform ablation by mixing the GGUF weights of Swift-1.5 and ConwayResearch/Underdog-Saluki-27B-1.0.
- granite-3.3-8b-base↗
ibm-granite
Granite-3.3-8B-Base is a decoder-only language model with a 128K token context window. It improves upon Granite-3.1-8B-Base by adding support for Fill-in-the-Middle (FIM) using specialized tokens, enabling the model to generate content conditioned on both prefix and suffix. This makes it well-suited for code completion tasks. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.3 models for languages beyond these 12 languages. **Intended Use:** Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, and other long-context tasks. All Granite Base models are able to handle these tasks as they were trained on a large amount of data from various domains. Moreover, they can serve as baseline to create specialized models for specific application scenarios.
- t5-small-txtsql↗
NeuML
T5 small fine-tuned to generate txtai SQL. This model takes natural language queries and builds txtai-compatible SQL statements. This model is an example of creating a custom query syntax that can be translated into SQL txtai can understand. Any query syntax can be created. This one supports English but a similar strategy can be deployed to support other languages. Natural language can be translated to functions, query clauses, column selection and more.
- MiniCPM-V-4.7-1B-abliterated-heretic-GGUF↗
Abiray
This repository provides quantized **GGUF** checkpoints for **MiniCPM-V 4.7 1B**, based on the refusal-abliterated weights created via **Heretic v1.4.0**. MiniCPM-V 4.7 1B is an ultra-efficient on-device multimodal large language model (MLLM) developed by OpenBMB. This version has been neutralized against standard refusal directions while retaining full multimodal vision and reasoning capabilities. The ablation was performed using **Heretic v1.4.0** to eliminate refusal bias while keeping Kullback–Leibler (KL) divergence near reference baseline: