Meta-Llama-3.1-405B-llamafile
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Meta-Llama-3.1-70B-Instruct-llamafile
mozilla-ai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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View details ↗Meta-Llama-3.1-8B-Instruct-llamafile
mozilla-aiThis is a large language model that was released by Meta on 2024-07-23. It was fine-tuned by Meta to follow your instructions. It's big enough to be capable of being put to serious use, and it's small enough to be capable of running on most personal computers. Mozilla has packaged the LLaMA model into executable weights that we call llamafiles. This gives you the easiest fastest way to use the model on Linux, MacOS, Windows, FreeBSD, OpenBSD 7.3, and NetBSD systems you control on both AMD64 and ARM64. To get started, you need both the LLaMA weights, and the llamafile software. Both of them are included in a single file, which can be downloaded and run as follows:
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View details ↗Meta-Llama-3.1-8B-llamafile
mozilla-aiThis is a large language model that was released by Meta on 2024-07-23. It's big enough to be capable of being put to serious use, and it's small enough to be capable of running on most personal computers. This repo contains the base model, which has not been fine-tuned to follow instructions. Mozilla has packaged the LLaMA model into executable weights that we call llamafiles. This gives you the easiest fastest way to use the model on Linux, MacOS, Windows, FreeBSD, OpenBSD and NetBSD systems you control on both AMD64 and ARM64. You can then use the completion mode of the GUI to experiment with this model. You can prompt the model for completions on the command line too:
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View details ↗Meta-Llama-3-8B-Instruct-llamafile
mozilla-aiThis repository contains executable weights (which we call llamafiles) that run on Linux, MacOS, Windows, FreeBSD, OpenBSD, and NetBSD for AMD64 and ARM64. Running the following on a desktop OS will launch a tab in your web browser with a chatbot interface. llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023. It uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp binaries that run on the stock installs of six OSes for both ARM64 and AMD64.
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View details ↗Mistral-7B-Instruct-v0.2-llamafile
mozilla-aiWARNING: This README may contain inaccuracies. It was generated automatically by forking TheBloke/Mistral-7B-Instruct-v0.2-GGUF and piping the README through sed. Errors should be reported to jartine, and do not reflect TheBloke. You can also support his work on Patreon. llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023. It uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp binaries that run on the stock installs of six OSes for both ARM64 and AMD64. These quantised llamafilev2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d
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View details ↗Mistral-7B-Instruct-v0.3-llamafile
mozilla-aiThis repository contains executable weights (which we call llamafiles) that run on Linux, MacOS, Windows, FreeBSD, OpenBSD, and NetBSD for AMD64 and ARM64. The third edition of Mistral 7B was released on May 22th, 2024. It increases the vocabulary size to 32768, supports the v3 tokenizer, and introduces support for function calling. Assuming your system has at least 16GB of RAM, you can try running the following command which download, concatenate, and execute the model.
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View details ↗Mistral-Nemo-Instruct-2407-llamafile
mozilla-ai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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View details ↗Mixtral-8x7B-Instruct-v0.1-llamafile
mozilla-ai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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View details ↗OLMo-7B-0424-llamafile
mozilla-ai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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View details ↗Phi-3-medium-128k-instruct-llamafile
mozilla-aiThis is a large language model that was released by Microsoft on May 21st, 2024. It has an outstanding context size, which makes it good for summarizing and extracting information from large bodies of text. It's also been fine-tuned to follow your instructions. Mozilla has packaged the Phi-3 model into executable weights that we call llamafiles. This gives you the easiest fastest way to use the model on Linux, MacOS, Windows, FreeBSD, OpenBSD and NetBSD systems you control on both AMD64 and ARM64. Running the following on a desktop OS will launch a tab in your web browser with both a chatbot interface and a completion interface.
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View details ↗Phi-3-mini-4k-instruct-llamafile
mozilla-aiThis repository contains executable weights (which we call llamafiles) that run on Linux, MacOS, Windows, FreeBSD, OpenBSD, and NetBSD for AMD64 and ARM64. Assuming your system has at least 32GB of RAM, you can try running the following command which download, concatenate, and execute the model. Alternatively, you may download an official `llamafile` executable from Mozilla Ocho on GitHub, in which case you can use the Mixtral llamafiles as a simple weights data file.
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View details ↗rocket-3B-llamafile
mozilla-ai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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View details ↗tiny-pii-electra-small
mozilla-ai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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View details ↗TinyLlama-1.1B-Chat-v1.0-llamafile
mozilla-ai- Model creator: TinyLlama - Original model: TinyLlama-1.1B-Chat-v1.0 - Prebuilt llamafiles for each quantization format that can be executed to launch a web server or cli interface - GGUF weights data files for each quantization format, which require either the llamafile or llama.cpp software to run
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View details ↗TriLM-llamafile
mozilla-ai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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View details ↗WizardCoder-Python-34B-V1.0-llamafile
mozilla-ai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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View details ↗distilvit
Mozilla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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View details ↗llama-test-model
Mozilla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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View details ↗smart-tab-embedding
MozillaThis is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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View details ↗smart-tab-topic
Mozilla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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View details ↗whisperfile
Mozilla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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View details ↗Qwen-Image-2.1-PE-MTP-GGUF
mozopheGGUF builds of the Qwen-Image 2.1 prompt enhancers (PE), with the Qwen3.5 multi-token prediction (MTP) head included, so llama.cpp can use speculative decoding (`--spec-type draft-mtp`). These files are made for the llama.cpp backend of ComfyUI-Qwen-Image-2.1-PromptEnhancer-MTP, which downloads them automatically. They also work with a plain `llama-server` (see Usage). The PE models don't ship an MTP head. It is grafted from the base model they were fine-tuned from, `Qwen/Qwen3.5-9B` (the same 15 `mtp.*` tensors the ComfyUI node uses). Community GGUFs of these models have no MTP head and no Q8_0.
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View details ↗DPRNNTasNet-ks2_WHAM_sepclean
mpariente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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