言語から画像、オープンウェイトからAPIまで。能力と実際の費用を比較。
次のアイデアに合うAIを見つけましょう。
This is an uncensored version of unsloth/gpt-oss-20b-BF16 created with abliteration (see remove-refusals-with-transformers to know more about it). You can use this model in your applications by loading it with Hugging Face's `transformers` library:
翻訳待ち・原文を表示
This model is a fine-tuned version of huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated. It has been trained using TRL. Please refer to Quantization-Aware Training (QAT) for fine-tuning and quantization(huihui-ai/Huihui-gpt-oss-20b-mxfp4-abliterated-v2). **Advantages**: All core metrics (Loss/Acc/Entropy) improve synchronously, with a small gap between Eval and Train (<0.01), indicating strong generalization ability. Fine-tuning shows effect in just 400 steps, with high efficiency.
翻訳待ち・原文を表示
This is a mxfp4 version of huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated-v2 Reference OpenAI GPT-OSS Quantization Aware Training (QAT) & Quantized Deployment Ollama requires the latest version: v0.11.8
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
We re-quantized the models using llama.cpp and have uploaded the Q2, Q3, Q4, Q5, Q6, Q8, and BF16 quantized models. Stock llama.cpp does not support the `kolibri1` architecture yet. Apply `kolibri1-llama.cpp.patch` (included in this repo) to llama.cpp at upstream commit `836d571`, then build:
翻訳待ち・原文を表示
This is an uncensored version of poolside/Laguna-S-2.1 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. **Note** This is an English language model, and support for other languages is not very good. - **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.
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
This is an uncensored version of mistralai/Mistral-Small-3.2-24B-Instruct-2506 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. If you like it, please click 'like' and follow us for more updates. You can follow x.com/support_huihui to get the latest model information from huihui.ai.
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
This is an uncensored version of TokenRhythm/NeoHorse-1-4B 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 MTP and Visual components were extracted from the original Qwen/Qwen3.5-4B and can provide excellent support.
翻訳待ち・原文を表示
This is an uncensored version of deepreinforce-ai/Ornith-1.0-9B 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. **Note** We will directly perform ablation on the GGUF files from protoLabsAI/Ornith-1.0-9B-MTP-GGUF. Some weights quantized in Q3_K, Q4_K, Q5_K and Q6_K will be converted to Q8_0. This time, the weights of the first 5 layers, so the final weights will increase, but the change will not be significant. We performed a simple quantization comparison between Q8_0 and MXFP4 on the Q3 version of protoLabsAI/Ornith-1.0-9B-MTP-GGUF.
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
This is an uncensored version of Mia-AiLab/Qwable-3.6-27b created with abliteration. This ablation was performed entirely using llama.cpp's GGUF model, without relying on Transformers, For the specific ablation method, please refer to cvector-generator.
翻訳待ち・原文を表示
This is an uncensored version of Mia-AiLab/Qwable-3.6-27b created with abliteration. This ablation was performed entirely using llama.cpp's GGUF model, without relying on Transformers, For the specific ablation method, please refer to cvector-generator.
翻訳待ち・原文を表示
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。
This is an uncensored version of Qwen/Qwen3-30B-A3B-Instruct-2507 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. Ablation was performed using a new and faster method, which yields better results. You can use huihui_ai/qwen3-abliterated:30b-a3b-instruct-2507-q4_K_M directly,
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
月間トークン数を調整してAPI費用を比較。
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。
