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AI 模型 449

• Linq-Embed-Mistral↗

  Linq-AI-Research

  Linq-Embed-Mistral has been developed by building upon the foundations of the E5-mistral-7b-instruct and Mistral-7B-v0.1 models. We focus on improving text retrieval using advanced data refinement methods, including sophisticated data crafting, data filtering, and negative mining guided by teacher models, which are highly tailored to each task, to improve the quality of the synthetic data generated by LLM. These methods are applied to both existing benchmark dataset and highly tailored synthetic dataset generated via LLMs. Our efforts primarily aim to create high-quality triplet datasets (query, positive example, negative example), significantly improving text retrieval performance. Linq-Embed-Mistral performs well in the MTEB benchmarks (as of May 29, 2024). The model excels in retrieval tasks, ranking **`1st`** among all models listed on the MTEB leaderboard with a performance score of **`60.2`** . This outstanding performance underscores its superior capability in enhancing search precision and reliability. The model achieves an average score of **`68.2`** across 56 datasets in the MTEB benchmarks, making it the highest-ranking publicly accessible model and third overall. (Please note that NV-Emb-v1 and voyage-large-2-instruct, ranked 1st and 2nd on the leaderboard as of May 29, reported their performance without releasing their models.) This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses. Please refer to specific papers for more details:

• Huihui-Qwen3.6-27B-abliterated-MTP-GGUF↗

  huihui-ai

  This is an uncensored version of Qwen/Qwen3.6-27B 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. Please use the latest version of ggml-org/llama.cpp - **Risk of Sensitive or Controversial Outputs**: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.

• sec-bert-base↗

  nlpaueb

  SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications. SEC-BERT consists of the following models: * **SEC-BERT-BASE** (this model): Same architecture as BERT-BASE trained on financial documents. * **SEC-BERT-NUM**: Same as SEC-BERT-BASE but we replace every number token with a [NUM] pseudo-token handling all numeric expressions in a uniform manner, disallowing their fragmentation * **SEC-BERT-SHAPE**: Same as SEC-BERT-BASE but we replace numbers with pseudo-tokens that represent the number’s shape, so numeric expressions (of known shapes) are no longer fragmented, e.g., '53.2' becomes '[XX.X]' and '40,200.5' becomes '[XX,XXX.X]'. The model was pre-trained on 260,773 10-K filings from 1993-2019, publicly available at U.S. Securities and Exchange Commission (SEC) * We created a new vocabulary of 30k subwords by training a BertWordPieceTokenizer from scratch on the pre-training corpus. * We trained BERT using the official code provided in Google BERT's GitHub repository . * We then used Hugging Face's Transformers conversion script to convert the TF checkpoint in the desired format in order to be able to load the model in two lines of code for both PyTorch and TF2 users. * We release a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters). * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. * We were able to use a single Google Cloud TPU v3-8 provided for free from TensorFlow Research Cloud (TRC), while also utilizing GCP research credits. Huge thanks to both Google programs for supporting us!

• FLUX.1 Kontext Max↗

  Black Forest Labs

  FLUX.1 Kontext Max API（APIMart）：上下文感知的图像编辑与文生图，最多支持 4 张参考图，每次请求生成一张约 1MP 图片。可通过文本指令修改商品、背景、角色和风格，支持九种宽高比及自动比例、PNG/JPEG/WebP 输出、提示词增强和异步任务查询。开通会员，API 调用立省20%起。

• financial-roberta-large-sentiment↗

  soleimanian

  Financial-RoBERTa Financial-RoBERTa is a pre-trained NLP model to analyze sentiment of financial text including: Financial Statements, Earnings Announcements, Earnings Call Transcripts, Corporate Social Responsibility (CSR) Reports, Environmental, Social, and Governance (ESG) News, Financial News, Etc. Financial-RoBERTa is built by further training and fine-tuning the RoBERTa Large language model using a large corpus created from 10k, 10Q, 8K, Earnings Call Transcripts, CSR Reports, ESG News, and Financial News text. The model will give softmax outputs for three labels: Positive , Negative or Neutral . How to perform sentiment analysis: The easiest way to use the model for single predictions is Hugging Face's sentiment analysis pipeline, which only needs a couple lines of code as shown in the following example:

• Huihui-Qwen3.8-27B-abliterated-GGUF↗

  ahmed-amedo

  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.

• ner-bert-base-cased-pt-lenerbr↗

  pierreguillou

  **ner-bert-base-portuguese-cased-lenerbr** is a NER model (token classification) in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model pierreguillou/bert-base-cased-pt-lenerbr on the dataset LeNER_br by using a NER objective. Due to the small size of BERTimbau base and finetuning dataset, the model overfitted before to reach the end of training. Here are the overall final metrics on the validation dataset (*note: see the paragraph "Validation metrics by Named Entity" to get detailed metrics*): - **f1**: 0.8926146010186757 - **precision**: 0.8810222036028488 - **recall**: 0.9045161290322581 - **accuracy**: 0.9759397808828684 - **loss**: 0.18803243339061737 **Note**: the model pierreguillou/bert-base-cased-pt-lenerbr is a language model that was created through the finetuning of the model BERTimbau base on the dataset LeNER-Br language modeling by using a MASK objective. This first specialization of the language model before finetuning on the NER task improved a bit the model quality. To prove it, here are the results of the NER model finetuned from the model BERTimbau base (a non-specialized language model): - **f1**: 0.8716487228203504 - **precision**: 0.8559286898839138 - **recall**: 0.8879569892473118 - **accuracy**: 0.9755893153732458 - **loss**: 0.1133928969502449

• MiniMax-H3-X2-Detail-VAE↗

  speach1sdef178

  > **Status:** Working model + tested custom node + workflow + research report\ > **Starting point:** my own already-working MiniMax H3 2X VAE\ > **Scope:** this report intentionally begins with that completed 2X VAE. I created the base 2X VAE used for this project as well, but how that VAE was built is a separate story and is not covered here. This repository is the final practical outcome of the experiment. It contains a **2-in-1 MiniMax H3 X2 VAE**, an optional ComfyUI detail-enhancement node, an example workflow, and the full research article below. Load `MiniMax-H3-X2-Detail-v1.safetensors` with the normal ComfyUI **Load VAE** node and use it as the video VAE for MiniMax H3. For the actual video decode, replace the base ComfyUI VAE Decode node with **`MiniMax H3 VAE Decode (fast)`** from TripleHeadedMonkey/ComfyUI-MiniMaxH3_LatentUpscaler. This node is required for the released 2X workflow.

• deepseek-coder-33B-instruct-GPTQ↗

  TheBloke

  Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches.

• lilt-xlm-roberta-base↗

  nielsr

  This model is created by combining the Language-Independent Layout Transformer (LiLT) with XLM-RoBERTa, a multilingual RoBERTa model (trained on 100 languages).