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vit-base-violence-detection
jaranohaalThis is a Vision Transformer (ViT) model fine-tuned for violence detection. The model is based on google/vit-base-patch16-224-in21k and has been trained on the Real Life Violence Situations dataset from Kaggle to classify images into violent or non-violent categories. The model is intended for use in applications where detecting violent content in images is necessary. This can include: model = ViTForImageClassification.from_pretrained('jaranohaal/vit-base-violence-detection') feature_extractor = ViTFeatureExtractor.from_pretrained('jaranohaal/vit-base-violence-detection')
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View details ↗Qwen3.8-27B-OBLITERATED
Jaredhollins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 ↗kev-0.5b
jaredpalmer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 ↗kev-0.6b
jaredpalmer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 ↗kev-0.8b
jaredpalmerKev-0.8B is a decision model. It reads one document (the *state*) and a set of typed questions about it, and returns a calibrated probability distribution over the options supplied with each question, in a single forward pass and without generating text. It implements TypeSafe's public System One API (`POST /v1/systemone`), so the TypeSafe SDK works against it unchanged. It is the smallest Kev: a LoRA adapter and a pointer head on Qwen3.5-0.8B-Base that runs on a laptop or a 4 GB GPU. It is intended where memory or cost rules out the larger sizes and the task resembles what it was trained on; out of domain it is markedly less accurate than Kev-4B. This card describes the checkpoint in Kev 1.0, first published on 2026-09-24. **Input.** A state (text, or a JSON object or array rendered as labelled text) and any number of named questions, each of one of three types: Each question is answered as its own row that continues from the shared state, so questions cannot influence one another; the state is computed once and cached.
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View details ↗kev-27b
jaredpalmer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 ↗kev-4b
jaredpalmerKev-4B is a decision model. It reads one document (the *state*) and a set of typed questions about it, and returns a calibrated probability distribution over the options supplied with each question, in a single forward pass and without generating text. It is intended for developers who classify, route, triage or check documents and who need probabilities that can be thresholded, for example to send uncertain cases to human review. It implements TypeSafe's public System One API (`POST /v1/systemone`), so the TypeSafe SDK works against it unchanged. It is a LoRA adapter and a pointer head on Qwen3.5-4B-Base, small enough for one 24 GB GPU or a 32 GB Apple Silicon Mac. This card describes the checkpoint in Kev 1.0, first published on 2026-09-24. **Input.** A state (text, or a JSON object or array rendered as labelled text) and any number of named questions, each of one of three types: Each question is answered as its own row that continues from the shared state, so questions cannot influence one another; the state is computed once and cached.
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View details ↗kev-9b
jaredpalmerKev-9B is a decision model. It reads one document (the *state*) and a set of typed questions about it, and returns a calibrated probability distribution over the options supplied with each question, in a single forward pass and without generating text. It is intended for developers who classify, route, triage or check documents and who need probabilities that can be thresholded, for example to send uncertain cases to human review. It implements TypeSafe's public System One API (`POST /v1/systemone`), so the TypeSafe SDK works against it unchanged. It is a LoRA adapter and a pointer head on Qwen3.5-9B-Base and fits one 24 GB-class GPU. This card describes version 2, released on 2026-09-30 and included in Kev 1.0. **Input.** A state (text, or a JSON object or array rendered as labelled text) and any number of named questions, each of one of three types: Each question is answered as its own row that continues from the shared state, so questions cannot influence one another; the state is computed once and cached.
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View details ↗Qwen3.8-27B-Uncensored-GGUF
jaromerUncensored Qwen3.8-27B, published as GGUF quantizations with the multi-token prediction (MTP) head retained and verified. Refusal behaviour has been **substantially reduced**, not eliminated. See Measured behaviour for the numbers. Capabilities, training data, and architecture are otherwise unchanged. > **MTP tensors verified, not assumed.** Abliteration drops the `mtp.*` tensors: the model > is re-saved through transformers, which does not carry the MTP head, while `config.json` > still advertises it. They are grafted back from the base checkpoint and every file is > inspected after quantization — see Method and Verification.
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View details ↗GLM-5.3-Flash-NestQuant-1.5-4bit
jarrelscy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 ↗GLM-5.3-Vision-NestQuant-2-4bit
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View details ↗aya-23-8B-llamafile
jartineThis repository contains executable weights (which we call llamafiles) that run on Linux, MacOS, Windows, FreeBSD, OpenBSD, and NetBSD for AMD64 and ARM64. 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. The maximum context size of this model is 8192 tokens. These llamafiles use a default context size of 512 tokens. Whenever you need the maximum context size to be available with llamafile for any given model, you can pass the `-c 0` flag. The temperature on these llamafiles is set to zero by default, because it helps. This can be changed, e.g. `--temp 0.8`.
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View details ↗WARNING: This README may contain inaccuracies. It was generated automatically by forking TheBloke/dolphin-2.5-mixtral-8x7b-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 Mixtral llamafiles are compatible with llama.cpp from December 13th onwards. Other clients/libraries may not work yet.
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View details ↗gemma-2-27b-it-llamafile
jartine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 ↗gemma-2-9b-it-llamafile
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