MyModes-Pretrain
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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。
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This is the same model as SantaCoder but it can be loaded with transformers >=4.28.1 to use the GPTBigCode architecture. We refer the reader to the SantaCoder model page for full documentation about this model There are two versions (branches) of the model: * `main`: Uses the `gpt_bigcode` model. Requires the bigcode fork of transformers. * `main_custom`: Packaged with its modeling code. Requires `transformers>=4.27`. Alternatively, it can run on older versions by setting the configuration parameter `activation_function = "gelu_pytorch_tanh"`. The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. `# the following function computes the sqrt`) or write a function signature and docstring and let the model complete the function body.
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The SantaCoder models are a series of 1.1B parameter models trained on the Python, Java, and JavaScript subset of The Stack (v1.1) (which excluded opt-out requests). The main model uses Multi Query Attention, a context window of 2048 tokens, and was trained using near-deduplication and comment-to-code ratio as filtering criteria and using the Fill-in-the-Middle objective. In addition there are several models that were trained on datasets with different filter parameters and with architecture and objective variations. The `final` model is the best performing model and was trained twice as long (236B tokens) as the others. This checkpoint is the default model and available on the `main` branch. All other checkpoints are on separate branches with according names. The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. `# the following function computes the sqrt`) or write a function signature and docstring and let the model complete the function body.
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StarCoder2-15B model is a 15B parameter model trained on 600+ programming languages from The Stack v2, with opt-out requests excluded. The model uses Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and was trained using the Fill-in-the-Middle objective on 4+ trillion tokens. The model was trained with NVIDIA NeMo™ Framework using the NVIDIA Eos Supercomputer built with NVIDIA DGX H100 systems. The model was trained on GitHub code as well as additional selected data sources such as Arxiv and Wikipedia. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. Here are some examples to get started with the model. You can find a script for fine-tuning in StarCoder2's GitHub repository.
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1. Model Summary 2. Use 3. Limitations 4. Training 5. License 6. Citation StarCoder2-3B model is a 3B parameter model trained on 17 programming languages from The Stack v2, with opt-out requests excluded. The model uses Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and was trained using the Fill-in-the-Middle objective on 3+ trillion tokens. - **Project Website:** bigcode-project.org - **Paper:** Link - **Point of Contact:** contact@bigcode-project.org - **Languages:** 17 Programming languages
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1. Model Summary 2. Use 3. Limitations 4. Training 5. License 6. Citation StarCoder2-7B model is a 7B parameter model trained on 17 programming languages from The Stack v2, with opt-out requests excluded. The model uses Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and was trained using the Fill-in-the-Middle objective on 3.5+ trillion tokens. - **Project Website:** bigcode-project.org - **Paper:** Link - **Point of Contact:** contact@bigcode-project.org - **Languages:** 17 Programming languages
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This is a 164M parameters model with the same architecture as StarCoder (8k context length, MQA & FIM). It was trained on the Python data from StarCoderData for ~6 epochs which amounts to 100B tokens. The model was trained on GitHub code, to assist with some tasks like Assisted Generation. For pure code completion, we advise using our 15B models StarCoder or StarCoderBase. Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:
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A collection of GGUF for Illustrious models just to help low memory people and do some experiences: - A-Mix - https://civitai.com/models/1692152 - Animagine - https://civitai.com/models/260267 - Bismuth Illustrious Mix - https://civitai.com/models/1359028 - Bubble hentai - https://civitai.com/models/1164420 - Chaos - https://civitai.com/models/2125236/chaos - CherryMIX_XL - https://civitai.com/models/1079112 - CyberRealistic - https://civitai.com/models/1125067 - GoldenHour - https://civitai.com/models/2433139 - GSBeyondReality - https://civitai.com/models/2455942 - Hassaku - https://civitai.com/models/140272 - Holly Mix - https://civitai.com/models/959490 - iLustMix - https://civitai.com/models/1110783 - Illustrij - https://civitai.com/models/1025051 - JANKU - https://civitai.com/models/1277670 - MatureRitual - https://civitai.red/models/994401/matureritual-illustrious - Nova Anime - https://civitai.com/models/376130 - Nova Furry - https://civitai.com/models/503815 - Nova Orange - https://civitai.com/models/967405 - Nova Reality DMD2 - https://civitai.com/models/453428?modelVersionId=2439777 - One Obsession - https://civitai.com/models/1318945 - One obsession Branch - https://civitai.com/models/1368727 - PerfectDeliberate - https://civitai.com/models/24350 - Poly-Goth Gloss - https://civitai.com/models/2506452 - Prefect illustrious - https://civitai.com/models/1224788 - Prefectious XL NSFW - https://civitai.com/models/992378 - RagIllustriousMix - https://civitai.com/models/1491577 - RealPhotoIL - https://civitai.com/models/2041366 - Ri-Mix Omega - https://civitai.com/models/996495 - SmoothMix - https://civitai.com/models/1695253 - SmoothMix Ultimate - https://civitai.com/models/2311905 - TovaMix - https://civitai.com/models/2284900 - Unholy Desire Mix - https://civitai.com/models/1307857 - WAI-illustrious-SDXL - https://civitai.com/models/827184 What I noticed is that Q6 or lower, will increse the s/it to double, not sure why... If you have a specific modelt hat you want the conversion, you can ask that I'll try to do it
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This is the direct conversion to GGUF from the model Wan2.2-Remix (T2V&I2V) - https://civitai.com/models/2003153 =================================================================================== if you would like to help me, it seems that runpod has a Refer thing - https://runpod.io?ref=d2452mau
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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。