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AI モデル 84757

• potion-multilingual-128M↗

  minishlab

  This Model2Vec model is pre-trained using Tokenlearn on all languages in the C4 dataset. It is a distilled version of the BAAI/bge-m3 Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. potion-multilingual-128M is a multilingual model, trained on 101 languages, and is capable of generating embeddings for any text in any language. The model produces 256 dimensional embeddings, and has a theoretically unlimited context length since embeddings are static (pre-computed). The Model2Vec library is the fastest and most lightweight way to run Model2Vec models.

• tokyotech-llm-Swallow-MS-7b-instruct-v0.1-gguf↗

  mmnga

  mistral mmnga/tokyotech-llm-Swallow-MS-7b-instruct-v0.1-gguf mmnga/tokyotech-llm-Swallow-7b-plus-hf-gguf mmnga/tokyotech-llm-Swallow-MS-7b-v0.1-gguf mmnga/tokyotech-llm-Swallow-MX-8x7b-NVE-v0.1-gguf llama2 mmnga/tokyotech-llm-Swallow-7b-instruct-v0.1-gguf mmnga/tokyotech-llm-Swallow-13b-instruct-v0.1-gguf mmnga/tokyotech-llm-Swallow-70b-instruct-v0.1-gguf

• bert-base-cased-goemotions-original↗

  monologg

• Caelum-G4-38B-A12.5B-GGUF↗

  mradermacher

• Crystal-31B-RL-i1-GGUF↗

  mradermacher

• cuckoo-starling-7B-GGUF↗

  mradermacher

• dolphin-2.2-mistral-7b-i1-GGUF↗

  mradermacher

• dolphin-2.9.1-yi-1.5-34b-i1-GGUF↗

  mradermacher

• FrankenLimmy-10B-passthrough-GGUF↗

  mradermacher

• GameScaling-9B-S3-GGUF↗

  mradermacher

  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)