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It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains. CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
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> 🚨 **Update:** This checkpoint is deprecated, please use https://huggingface.co/almanach/camembert-base instead 🚨 It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains. CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains. CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
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CamemBERTav2 is a French language model pretrained on a large corpus of 275B tokens of French text. It is the second version of the CamemBERTa model, which is based on the DebertaV2 architecture. CamemBERTav2 is trained using the Replaced Token Detection (RTD) objective with 20% mask rate on 275B tokens on 32 H100 GPUs. The dataset used for training is a combination of French OSCAR dumps from the CulturaX Project, French scientific documents from HALvest, and the French Wikipedia. The model is a drop-in replacement for the original CamemBERTa model. Note that the new tokenizer is different from the original CamemBERTa tokenizer, so you will need to use Fast Tokenizers to use the model. It will work with `DebertaV2TokenizerFast` from `transformers` library even if the original `DebertaV2TokenizerFast` was sentencepiece-based. The new update includes: - Much larger pretraining dataset: 275B unique tokens (previously ~32B) - A newly built tokenizer based on WordPiece with 32,768 tokens, addition of the newline and tab characters, support emojis, and better handling of numbers (numbers are split into two digits tokens) - Extended context window of 1024 tokens
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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AWQ W4A16 (group_size 128, symmetric) quantization of trohrbaugh/gemma-4-31b-it-heretic-ara — a Heretic-ARA abliterated derivative of google/gemma-4-31b-it. > ⚠️ **Decensored model.** Safety guardrails have been deliberately removed. > Research and experimentation only. See full disclaimer below. Approximate VRAM for inference at this quantization (W4A16-G128): - **Weights** (quantized language model + unquantized vision tower, all in one safetensors): **~19 GB** - **KV cache** (per request, grows with context length): ~1-4 GB at 32K context, more at longer contexts - **Recommended**: Single 24 GB GPU (RTX 3090/4090, A10G) for standard context up to ~32K, or single 48 GB GPU (L40S/A6000) for long context / batch serving - **Long context (128K+)**: 48-80 GB recommended due to KV cache growth
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Quantized **Krea 2** checkpoints for ComfyUI — about **2x faster** and **a third the size** of the usual FP8 version, with no calibration dataset needed, on both **Krea 2 Turbo** (distilled, 8 steps) and the **base** release. This repo holds the custom nodes, the All-in-One builder, and the `quantize_krea2.py` conversion script; the `.safetensors` files live on Hugging Face. Works on **any modern NVIDIA GPU** — INT8/W4A4 tensor cores go back to Turing (RTX 20-series). Benchmarked on an RTX 3090 (Ampere). Everything here was built from scratch against ComfyUI's own quantization backend and is 100% reproducible. > **Requires a cu130 (CUDA 13) or newer PyTorch build.** ComfyUI disables `comfy_kitchen`'s > CUDA backend on older torch builds, which silently drops every quantized checkpoint onto a > pure-Python fallback that is *slower than bf16*. If these checkpoints are slower than FP8 > for you, see TROUBLESHOOTING.md.
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This repository provides an optimized **FP8 (float8_e4m3fn) weight-only quantized version** of the newly released **Krea 2 OSS (Turbo)** transformer. This optimization reduces the model size from the original **24.76 GiB (BF16)** down to **12.01 GiB**, making it highly accessible and runnable on standard consumer hardware (such as 16GB and 24GB GPUs) without sacrificing output quality. - **Original Model Creators**: All credit goes to KREA.ai for the original research, architecture, and weights. - **License**: This model is subject to the **KREA 2 License Agreement**. Please read and comply with the official license terms before using these weights: KREA 2 Licensing Terms. - **Purpose**: This repository is a community-contributed utility. It does not claim ownership of the original model or architecture. Its sole purpose is to provide optimized, consumer-hardware-friendly weights for the open-source community.
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Quantized GGUF weights of the state-of-the-art **Qwen-Image 2.1** diffusion model for **ComfyUI** using ComfyUI-GGUF. These models allow running Qwen-Image 2.1 on consumer GPUs with significantly reduced VRAM footprint while preserving maximum visual quality, text-rendering capabilities, and composition fidelity. 1. **Diffusion Model (GGUF):** - Download any of the `.gguf` files from this repo (e.g. `qwen_image_2.1_Q8_0.gguf`). - Place it in: `ComfyUI/models/diffusion_models/` (or `ComfyUI/models/unet/`)
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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可以调整厂商、分类或输入输出条件；新模型正持续收录。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。
