저장한 도구도구 추천
FOXAI SEARCH

전체 검색

AI 모델 2119

  • gemma-4-31B-it-qat-NVFP4-Blackwell↗

    melcheikh

    > [!TIP] > **There is a newer version of this model:** > gemma-4-31B-it-qat-NVFP4-mse-Blackwell. > Same base model and calibration set, but with MSE-based weight scaling > instead of absmax, which keeps outliers from crushing the rest of each block. > This repo stays as it is so existing deployments don't change under their feet. Gemma 4 31B IT quantized to NVFP4 for native FP4 inference on NVIDIA Blackwell GPUs. Quantized from Google's unquantized QAT weights (`google/gemma-4-31B-it-qat-q4_0-unquantized`) with NVIDIA Model Optimizer (`modelopt` 0.44.0), default absmax scaling. - **Base Model:** google/gemma-4-31B-it-qat-q4_0-unquantized - **Quantization Format:** `NVFP4` (native FP4 on Blackwell) - **Group Size:** 16 (per-group scaling) - **Excluded Modules:** `lm_head`, `model.embed_vision*`, `model.vision_tower*` (kept in high precision) - **KV cache scales:** none in the checkpoint; use `--kv-cache-dtype fp8` and let vLLM scale dynamically - **Producer:** NVIDIA Model Optimizer (`modelopt` 0.44.0)

  • opensearch-neural-sparse-encoding-multilingual-v1↗

    opensearch-project

    The model should be selected considering search relevance, model inference and retrieval efficiency(FLOPS). We benchmark models' performance on **MIRACL** benchmark (we exclude th since the uncased backbone can not encode it). **We recommend to use it with max_ratio pruning.** - **Paper**: Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers - **Fine-tuning sample**: opensearch-sparse-model-tuning-sample This is a learned sparse retrieval model. It encodes the documents to 105879 dimensional **sparse vectors**. For queries, it just use a tokenizer and a weight look-up table to generate sparse vectors. The non-zero dimension index means the corresponding token in the vocabulary, and the weight means the importance of the token. And the similarity score is the inner product of query/document sparse vectors.

  • tabpfn_2_5↗

    Prior-Labs

    TabPFN-2.5 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/tabPFN. For more examples (e.g. how to train a regressor), see the github repo: https://github.com/PriorLabs/tabPFN! Regression and classification tasks with ≤50 000 samples and ≤2000 features in structured tabular format.

  • resnet34.tv_in1k↗

    timm

    This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 21.8 - GMACs: 3.7 - Activations (M): 3.7 - Image size: 224 x 224 - **Papers:** - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **Original:** https://github.com/pytorch/vision Explore the dataset and runtime metrics of this model in timm model results.

  • GLM-4-9B-0414↗

    zai-org

    The GLM family welcomes new members, the **GLM-4-32B-0414** series models, featuring 32 billion parameters. Its performance is comparable to OpenAI’s GPT series and DeepSeek’s V3/R1 series. It also supports very user-friendly local deployment features. GLM-4-32B-Base-0414 was pre-trained on 15T of high-quality data, including substantial reasoning-type synthetic data. This lays the foundation for subsequent reinforcement learning extensions. In the post-training stage, we employed human preference alignment for dialogue scenarios. Additionally, using techniques like rejection sampling and reinforcement learning, we enhanced the model’s performance in instruction following, engineering code, and function calling, thus strengthening the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves good results in engineering code, Artifact generation, function calling, search-based Q&A, and report generation. In particular, on several benchmarks, such as code generation or specific Q&A tasks, GLM-4-32B-Base-0414 achieves comparable performance with those larger models like GPT-4o and DeepSeek-V3-0324 (671B). **GLM-Z1-32B-0414** is a reasoning model with deep thinking capabilities. This was developed based on GLM-4-32B-0414 through cold start, extended reinforcement learning, and further training on tasks including mathematics, code, and logic. Compared to the base model, GLM-Z1-32B-0414 significantly improves mathematical abilities and the capability to solve complex tasks. During training, we also introduced general reinforcement learning based on pairwise ranking feedback, which enhances the model's general capabilities. **GLM-Z1-Rumination-32B-0414** is a deep reasoning model with rumination capabilities (against OpenAI's Deep Research). Unlike typical deep thinking models, the rumination model is capable of deeper and longer thinking to solve more open-ended and complex problems (e.g., writing a comparative analysis of AI development in two cities and their future development plans). Z1-Rumination is trained through scaling end-to-end reinforcement learning with responses graded by the ground truth answers or rubrics and can make use of search tools during its deep thinking process to handle complex tasks. The model shows significant improvements in research-style writing and complex tasks.

  • gte-Qwen2-7B-instruct↗

    Alibaba-NLP

    **gte-Qwen2-7B-instruct** is the latest model in the gte (General Text Embedding) model family that ranks **No.1** in both English and Chinese evaluations on the Massive Text Embedding Benchmark MTEB benchmark (as of June 16, 2024). Recently, the **Qwen team** released the Qwen2 series models, and we have trained the **gte-Qwen2-7B-instruct** model based on the Qwen2-7B LLM model. Compared to the gte-Qwen1.5-7B-instruct model, the **gte-Qwen2-7B-instruct** model uses the same training data and training strategies during the finetuning stage, with the only difference being the upgraded base model to Qwen2-7B. Considering the improvements in the Qwen2 series models compared to the Qwen1.5 series, we can also expect consistent performance enhancements in the embedding models. - Integration of bidirectional attention mechanisms, enriching its contextual understanding. - Instruction tuning, applied solely on the query side for streamlined efficiency - Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks.

  • bert_uncased_L-8_H-512_A-8↗

    Google

    This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking). We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.

  • Lightricks-LTX-2-DISTILLED-10-Eros↗

    ibyteohdear

    An uncensored video generation model based on LTX 2.3 supporting both t2v and i2v natively, as well as all of the other ltx 2.3 formats. Support the next version of the project, even just a few dollars would go a long way: **Kofi** **Get Started:** To get started with the model, I recommend downloading either of the dev versions, (fp8mixed or bf16) and downloading the distill lora provided. By the way, I'm aware the workflows contain sulphur_final right now, just use the lora or use the full models, don't use both at the same time.

  • Qwen3-VL-4B-Instruct-NVFP4↗

    JEILDLWLRMA

    This is a quantized version of Qwen/Qwen3-VL-4B-Instruct using **SmoothQuant + NVFP4** across all text linear layers. Applied with strength 0.8 for activation smoothing before quantization: - Q/K/V projections ← input_layernorm - Gate/Up projections ← post_attention_layernorm - **Base Model**: Qwen/Qwen3-VL-4B-Instruct (4.4B parameters) - **Quantization Method**: compressed-tensors (llm-compressor) - **Model Size**: ~4.1 GB (reduced from ~8.9 GB BF16, ~54% compression) - **Calibration**: 512 samples from flickr30k, max_seq_length=2048

  • TIPO-500M-ft↗

    KBlueLeaf

    500M LLaMA arch model trained for TIPO. Tech Report: https://arxiv.org/abs/2411.08127 In this project, we introduce "TIPO" (**T**ext to **I**mage with text presampling for **P**rompt **O**ptimization), an innovative framework designed to significantly enhance the quality and usability of Text-to-Image (T2I) generative models. TIPO utilizes the Large Language Models (LLMs) to perform "Text Presampling" within the inference pipeline of text-to-image generative modeling. By refining and extending user input prompts, TIPO enables generative models to produce superior results with minimal user effort, making T2I systems more accessible and effective for a wider range of users. Use updated version of DTG extension (renamed to z-tipo-extension), current version of z-tipo-extension support stable-diffusion-webui, stable-diffusion-webui-forge and ComfyUI. SD-Next haven't been tested. https://github.com/KohakuBlueleaf/z-tipo-extension