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AI モデル 2119
- wide_resnet50_2.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): 68.9 - GMACs: 11.4 - Activations (M): 14.4 - Image size: 224 x 224 - **Papers:** - Wide Residual Networks: https://arxiv.org/abs/1605.07146 - 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.
- Samantha-Qwen-2-7B-GGUF↗
bartowski
- Qwen3.6-35B-A3B-GGUF↗
byteshape
This is a GGUF-quantized version of Qwen3.6-35B-A3B produced with **ByteShape's ShapeLearn**, which learns the optimal datatype per tensor to maintain high quality even at very low bitlengths. To learn more about ShapeLearn and to see detailed benchmarks across GPUs, CPUs, and even the Raspberry Pi, please visit our blog. Pick a model from the tables below and click **Get llama.cpp command** to get a ready-to-run command with all the correct sampling parameters for this model.
- Ternary-Bonsai-2-27B-Abliterated-swift-cap-PQ2_0-MTP↗
hotdogs
**BoldingBuilds' abliterated Bonsai 2 v2, with two SVD-LoRAs merged in at 0.4 each:** `hotdogs/swift_unc_qwen3.8-27B_lora` (abliteration + Swift-1.5 reasoning-efficiency) and `hotdogs/thinkingcap_qwen3.8-27B_svd_lora` (ThinkingCap token-efficient reasoning). Same 27B ternary runtime as the base — **PQ2_0 packing, Hadamard latent, MTP head, 262,144-token context**, ~7.6 GB on disk. Vision projector included. > ⚠️ **This file needs PrismML's llama.cpp fork.** PQ2_0 does **not** load in mainline llama.cpp, Ollama, LM Studio or llama.app. See Requirements.
- stack-edu-classifier-javascript↗
HuggingFaceTB
This is a classifier for scoring the educational value of code files in The Stack v2 dataset, it is a finetuned version of bigcode/starencoder with a classification head on code files annotated by Llama3.1-70B-Instruct. We use this classifier for building Stack-Edu dataset used for training SmolLM2, see paper. Each classifier is trained on one programming language. While the classifier performs well in distinguishing high-quality code in its target language (JS in this case), there are some limitations: - Scope: The model's performance might change for other datasets, in particular for out of distribution samples. The classifier's context is 1024 tokens, which might not be sufficient to assess the quality of some long code files. - Bias: The model's performance is dependent on the quality and representativeness of the training data and the LLM used for the annotation. Biases in both can affect the classifier's judgments. It might overfit to thoroughly commented code. - Context: The classifier evaluates individual code files without considering broader context, which might impact its effectiveness in certain scenarios.
- Qwen3.5-122B-A10B-abliterix-NVFP4↗
bjk110
NVFP4 (W4A4) quantization of `wangzhang/Qwen3.5-122B-A10B-abliterix` — the 122B/A10B Abliterix-abliterated (uncensored) Qwen3.5 MoE — packaged for vLLM serving on NVIDIA Blackwell hardware (DGX Spark GB10 / SM121). The Abliterix-trained uncensored behavior (0.5% refusal rate, KL divergence 0.0115 vs the Qwen3.5-122B-A10B baseline) is preserved while dropping weight memory from BF16 (~230 GB) to NVFP4 (~70 GB) — fitting comfortably on two DGX Spark nodes (2 × 119 GiB unified memory) with headroom for KV-cache, with the same compressed-tensors runtime path as the FP8 sibling. Compared to the FP8 W8A8 variant (bjk110/Qwen3.5-122B-A10B-abliterix-FP8, 116 GB), this NVFP4 build cuts another ~40% off-disk and unlocks the **FlashInfer-CUTLASS NVFP4 MoE backend** for higher throughput on Blackwell.
- gliclass-base-v3.0↗
knowledgator
This is an efficient zero-shot classifier inspired by GLiNER work. It demonstrates the same performance as a cross-encoder while being more compute-efficient because classification is done at a single forward path. It can be used for `topic classification`, `sentiment analysis`, and as a reranker in `RAG` pipelines. The model was trained on logical tasks to induce reasoning. LoRa adapters were used to fine-tune the model without destroying the previous knowledge.
- stable-diffusion-xl-base-1.0↗
Stability AI
SDXL consists of an ensemble of experts pipeline for latent diffusion: In a first step, the base model is used to generate (noisy) latents, which are then further processed with a refinement model (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/) specialized for the final denoising steps. Note that the base model can be used as a standalone module. Alternatively, we can use a two-stage pipeline as follows: First, the base model is used to generate latents of the desired output size. In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img") to the latents generated in the first step, using the same prompt. This technique is slightly slower than the first one, as it requires more function evaluations. For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference) and for which new functionalities like distillation will be added over time. Clipdrop provides free SDXL inference.
- flux-3-image-klein↗
unsloth
> Ungated mirror of black-forest-labs/flux-3-image-klein, republished by Unsloth so it > can be downloaded without a Hub gate. The weights are unmodified. See NOTICE.md and LICENSE.md > in this repo for the terms, which are the upstream terms and are unchanged. FLUX 3 Image Klein is a 5.6B open weights model for text-to-image generation and image editing. It takes a prompt and up to 10 reference images and returns one image. Text-to-image, single-reference edits and multi-reference compositions run through the same model, with Qwen3-VL-4B-Instruct as the text encoder. This repository holds the distilled checkpoint for generation and image editing. For fine-tuning, LoRA training and research, use FLUX 3 Image Klein Base.
- WizardMath-7B-V1.1↗
WizardLMTeam
Before model training, we carefully and rigorously checked all the training data, and used multiple deduplication methods to verify and prevent data leakage on GSM8k and MATH test set. Please use **the same systems prompts strictly** with us, and we do not guarantee the accuracy of the **quantified versions**.