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。
We have a free Google Colab Tesla T4 notebook for Phi-3.5 (mini) here: https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
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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。
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We have converted Phi-4 to Llama's architecture for improved ease of use, better fine-tuning, and greater accuracy. Also contains Unsloth's Phi-4 bugfixes. We have a free Google Colab notebook for Phi-4 here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model.
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This is Phi-4-mini-instruct with our BUG FIXES. See our collection for versions of Phi-4 with our bug fixes including GGUF & 4-bit formats. Unsloth's Phi-4 Dynamic Quants is selectively quantized, greatly improving accuracy over standard 4-bit. We have a free Google Colab notebook for turning Phi-4 into a reasoning model: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_(14B)-GRPO.ipynb
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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。
基本情報を収録・詳細は順次追加
This is Phi-4-mini-instruct with our BUG FIXES. See our collection for versions of Phi-4 with our bug fixes including GGUF & 4-bit formats. Unsloth's Phi-4 Dynamic Quants is selectively quantized, greatly improving accuracy over standard 4-bit. We have a free Google Colab notebook for turning Phi-4 into a reasoning model: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_(14B)-GRPO.ipynb
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This is Phi-4-mini-instruct with our BUG FIXES. See our collection for versions of Phi-4 with our bug fixes including GGUF & 4-bit formats. Unsloth's Phi-4 Dynamic Quants is selectively quantized, greatly improving accuracy over standard 4-bit. We have a free Google Colab notebook for turning Phi-4 into a reasoning model: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_(14B)-GRPO.ipynb
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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。
Phi-4-mini-reasoning is a lightweight open model built upon synthetic data with a focus on high-quality, reasoning dense data further finetuned for more advanced math reasoning capabilities. The model belongs to the Phi-4 model family and supports 128K token context length. 📰 Phi-4-mini-reasoning Blog, and Developer Article 📖 Phi-4-mini-reasoning Technical Report 👩🍳 Phi Cookbook 🏡 Phi Portal 🖥️ Try It Azure Phi-4-mini-reasoning is designed for multi-step, logic-intensive mathematical problem-solving tasks under memory/compute constrained environments and latency bound scenarios. Some of the use cases include formal proof generation, symbolic computation, advanced word problems, and a wide range of mathematical reasoning scenarios. These models excel at maintaining context across steps, applying structured logic, and delivering accurate, reliable solutions in domains that require deep analytical thinking.
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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。
> [!NOTE] > You must use `--jinja` in llama.cpp to enable reasoning. Otherwise no token will be provided. > Our training data is a mixture of Q&A, chat format data in math, science, and coding. The chat prompts are sourced from filtered high-quality web data and optionally rewritten and processed through a synthetic data generation pipeline. We further include data to improve truthfulness and safety. We evaluated Phi-4-reasoning-plus using the open-source Eureka evaluation suite and our own internal benchmarks to understand the model's capabilities. More specifically, we evaluate our model on:
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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。
基本情報を収録・詳細は順次追加
Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. 📑 Technical Report | 📖 Blog | 🤗 Hugging Face | 🤖 ModelScope | 💻 GitHub | 🖥️ Demo > [!Note] > This repository contains the model weights and configuration files for **Qwen-AgentWorld-35B-A3B**, a native language world model trained for agentic environment simulation. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc.
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> Mirror of Qwen/Qwen-Image, republished by Unsloth. The weights > are unmodified. See NOTICE and the licence file in this repo for the terms, which are > the upstream terms and are unchanged. 💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    🖥️ Demo    |   💬 WeChat (微信)    |   🫨 Discord   
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🤖 ModelScope | 🤗 HuggingFace | 📑 Blog | 🖥️ Demo | 🫨 Discord | 💬 WeChat We are excited to open-source **Qwen-Image-2.1**, a unified text-to-image generation and image editing model in the Qwen family. With just **7B parameters in its visual generation component** (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility. - **Compact and Efficient** — A lightweight architecture with mixed-granularity attention and prefix KV cache reuse delivers strong image quality at low computational cost. - **Native Transparency, Unified Creation and Editing** — Generate regular or transparent (RGBA) images from text, edit transparent layers, and extract subjects from photographs—all in one model. - **Versatile Editing** — Support up to **10 reference images**, specify local edits via circles, painted annotations, or separate masks, and preserve identity for people and products. - **Realistic Textures and Refined Aesthetics** — Improved typography, portrait lighting, and fine details for more visually compelling results.
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This is an FP8 / INT8 quantized version of Qwen-Image-2.1, plus a pre-cast FP8 copy of its Qwen3-VL text encoder. - For higher quality, you can also run Dynamic FP8 on just 6GB of VRAM via offloading ( Everything here is safetensors rather than a pickle, so loading runs no arbitrary code. Needs a diffusers main build: Qwen-Image-2.1 support postdates the 0.40 release. 🤖 ModelScope | 🤗 HuggingFace | 📑 Blog | 🖥️ Demo | 🫨 Discord | 💬 WeChat
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This is a GGUF quantized version of Qwen-Image-2.1. unsloth/Qwen-Image-2.1-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision, per tensor, from a measured sensitivity scan. - Run these with Unsloth Desktop, stable-diffusion.cpp and more. A GGUF is the denoiser only, so it needs the VAE and the Qwen3-VL text encoder alongside it. - VAE: unsloth/Qwen-Image-2.1-FP8 `vae/qwen_image_2.1_vae_bf16.safetensors`. Text encoder: unsloth/Qwen3-VL-8B-Instruct-GGUF `Qwen3-VL-8B-Instruct-UD-Q4_K_XL.gguf`, the Dynamic 2.0 4-bit rung rather than the uniform `Q4_K_M`. Measured against the `Q4_K_M` encoder at a shared seed, with the denoiser and VAE held fixed: LPIPS 0.029, SSIM 0.959, 5.15 GB vs 5.03 GB, 36.5 s vs 39.0 s. Rendered with the Q4_K_M denoiser and the Q4_K_M text encoder, 1024x1024, 20 steps, cfg 6.0, euler.
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This is an FP8 / INT8 quantized version of Qwen-Image-2.1-Turbo, the 8-step distilled checkpoint of Qwen-Image-2.1. - Each file replaces the 14.2 GB bf16 `transformer` with a 7.1 to 7.3 GB one. - Same-seed LPIPS vs the bf16 Turbo model (lower is better): 0.037 INT8-ConvRot, 0.089 INT8, 0.125 FP8. INT8-ConvRot is the recommended file. - Turbo shares its text encoder and VAE with Qwen-Image-2.1 (text encoder byte-identical, VAE identical in bf16), so the FP8 text encoder and bf16 VAE in unsloth/Qwen-Image-2.1-FP8 work with these files as is. Built from `Qwen/Qwen-Image-2.1-Turbo` at revision `d65dbc9a7e8f6b5479e33dee6030eaab2a906509` with the same recipe as unsloth/Qwen-Image-2.1-FP8: norms, modulation, timestep embeddings and (for INT8) `txt_in` stay bf16, every other linear is quantized. Scores are LPIPS (AlexNet) against the bf16 Turbo transformer at the card's settings (8 steps, CFG 1, the shipped sigma schedule, 1024x1024), 8 prompts x 2 seeds, with the same bf16 text encoder in every run. Everything here is safetensors rather than a pickle, so loading runs no arbitrary code. Needs a diffusers main build with pipeline-configured sampling sigmas (PR #14950).
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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。
