Open-Weight Models

Model · Image and text to text

clef-flash-4bit

MLX Community

Cloudflare/clef-flash converted to MLX (4-bit) for Apple Silicon. Clef turns a state (text, JSON, images, or video) plus a schema of typed questions into a probability for every allowed option, in a single forward pass. It is not a chat model — mlxvlm.generate, mlxlm.generate, and LM Studio will load the backbone but produce meaningless text. Use the bundled clefmlx.py loader, which runs the backbone and the joint schema head. Images (PIL) and videos (frame arrays) go in images / videos, as in the original: See the original model card for the input format, question types, and benchmarks. - processorconfig.json is the original from Cloudflare/clef-flash; prompt/token layout matches the…

Model · Image and text to text

clef-flash-8bit

MLX Community

Cloudflare/clef-flash converted to MLX (8-bit) for Apple Silicon. Clef turns a state (text, JSON, images, or video) plus a schema of typed questions into a probability for every allowed option, in a single forward pass. It is not a chat model — mlxvlm.generate, mlxlm.generate, and LM Studio will load the backbone but produce meaningless text. Use the bundled clefmlx.py loader, which runs the backbone and the joint schema head. Images (PIL) and videos (frame arrays) go in images / videos, as in the original: See the original model card for the input format, question types, and benchmarks. - processorconfig.json is the original from Cloudflare/clef-flash; prompt/token layout matches the…

Model · Text classification

clefsift

Stephen Murray

ClefSift is an experimental English text classifier fine-tuned from Cloudflare/Clef. It returns scores for four document-level labels in one forward pass: These are provisional, process-based labels. The model sees writing style, not the author's actual workflow. Its output does not prove authorship. Do not use it as the sole basis for academic, employment, or moderation decisions. This release uses the selected v2 step-128 checkpoint. The additional rank-64 LoRA weights are merged into the BF16 backbone. The trained joint decision head is stored separately in FP32. This is a decision model, not a chat model. The retained config and loader use the Qwen3.5 architecture identifiers above. Pin…

Versioned model and runtime packages for the desktop client. Each package is verified by SHA-256

The model identifies candidate clinical evidence spans in English text. Its v3 schema has six core entity types and explicit attributes. Shared attributes are three complete, qualified, single-label groups: assertion (affirmed, hypothetical, negated, ruled out, uncertain), experiencer (patient, family, other), and time frame (current, recent, historical, future). The registry also defines family-specific choice and literal-span labels. Use the complete versioned schema in schemav3.json; do not substitute an open-ended or partial attribute schema. An absent axis in a source row was masked during training; it was not a negative or an implicit default. Defaults are ordinary explicit values…

This is the epoch 4 LoRA adapter from job 17089, trained to extract exact clinical evidence spans and explicit attributes under the ClinicalEvidence v3 span schema. It is an individual research adapter, not the final winner of the current multi-seed campaign. The repository contains a PEFT adapter. Load it on the pinned public base model, then pass the v3 schema. The base revision below is a verified public reference snapshot whose core runtime assets match the frozen base assets used in this campaign; it does not establish the historical resolved revision used by the training process. The code above is illustrative and was not executed as part of this card. Use schemav3.json for the…

The model identifies candidate clinical evidence spans in English text. Its v3 schema has six core entity types and explicit attributes. Shared attributes are three complete, qualified, single-label groups: assertion (affirmed, hypothetical, negated, ruled out, uncertain), experiencer (patient, family, other), and time frame (current, recent, historical, future). The registry also defines family-specific choice and literal-span labels. Use the complete versioned schema in schemav3.json; do not substitute an open-ended or partial attribute schema. An absent axis in a source row was masked during training; it was not a negative or an implicit default. Defaults are ordinary explicit values…

Model

clip-checkpoint

Felix Sari

Working implementation of CLIP for Multitask using a giant configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a step schedule. These are starting values in the script, not evidence of a completed…

Model

clip-classification-warmup

Jason H. Lopez

A small CLIP implementation for Classification, packaged with an explicit configuration and an initialization checkpoint. The large variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a exponential schedule. These are starting values in the script, not…

Model

clip-contrastive

Jacob Davis

This repository is a compact, custom PyTorch implementation of CLIP for Contrastive. The nano configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a step schedule. These are starting…

Model

clip-contrastive-lab27

Abdulrahman Alshehri

A small CLIP implementation for Contrastive, packaged with an explicit configuration and an initialization checkpoint. The xlarge variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses sgd with a linear warmup schedule. These are starting values in the script, not…

Model

clip-matching

Marcus HO

A small CLIP implementation for Matching, packaged with an explicit configuration and an initialization checkpoint. The xlarge variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a polynomial schedule. These are starting values in the script, not…

Model

clip-matching

Freja Andersson

This is an experimental CLIP codebase for Matching. It keeps the large setup intentionally manageable so architecture changes can be inspected before a full training run. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a constant warmup schedule. These are starting values in the script, not evidence of a completed run.…

Model

clip-matching

Julia Schmidt

Working implementation of CLIP for Matching using a huge configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a cosine schedule. These are starting values in the script, not evidence of a…

Model

clip-multitask

Daniil Kozlov

This is an experimental CLIP codebase for Multitask. It keeps the tiny setup intentionally manageable so architecture changes can be inspected before a full training run. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adam with a onecycle schedule. These are starting values in the script, not evidence of a completed run. For a…

Model

clip-multitask-base17-2024

Vitor PEREIRA

This repository is a compact, custom PyTorch implementation of CLIP for Multitask. The giant configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses sgd with a constant warmup schedule. These are…

Model

clip-retrieval21-2024

Olivia K. Taylor

This repository is a compact, custom PyTorch implementation of CLIP for Retrieval. The large configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a constant warmup schedule. These…

https://huggingface.co/openai/clip-vit-base-patch16 with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Example: Perform zero-shot image classification with the pipeline API. Example: Compute vision embeddings with CLIPVisionModelWithProjection. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

This repository is not a model. It holds the installer payload for Clips Kitty, a free, open-source AI video clipping app that runs entirely on your own Windows PC. It lives on Hugging Face for one dull reason: a GitHub release asset is capped at 2 GiB and the payload is roughly twice that, because the installer bundles everything the app needs rather than sending you off to install it yourself. They will not do anything on their own. Get the installer instead: One click, updates itself, and no Windows security prompt — Microsoft signed it. The Web Setup is about 1 MB. Run it, and it fetches the payload from here by itself, checks it against the SHA512 in latest.yml, and installs the app.…

Model · Text classification

clm

Ollaya

Ollaya package of Contrastive-LM/CLM-v0.1-8B and Qwen/Qwen3-8B by Contrastive-LM (projection heads) and the Qwen team (Qwen3-8B encoder). Ollaya runs open decision models locally, the way Ollama runs LLMs: typed questions in, calibrated answers out, behind a TypeSafe-compatible API. This repository holds only the files Ollaya derives, with no weights. Each graph is an ONNX export of the original model whose weights reference the authors' own weight files by byte offset, so ollaya pull downloads the weights from the upstream repositories, unmodified and pinned to a commit, and verifies their sha256. Each tag has an fp32 graph, used on CPU and GPU. Each tag also has decision.json (sequence…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

Model · Image and text to text

clover-1-150b-preview

Kitani

libraryname: transformers pipelinetag: image-text-to-text basemodel: - Qwen/Qwen3.8-27B - clover - mixture-of-experts - multimodal - reasoning - open-weights - custom-code Clover 1 150B Preview is the first public checkpoint of Clover 1, an experimental MoE model we're developing at Kitani. It's based on Qwen3.8-27B, but it isn't just a finetune with a different name. We kept much of Qwen3.8's underlying architecture, including the tokenizer, multimodal components, hybrid language backbone, embeddings, and LM head, while replacing the language model's dense FFNs with our own sparse mixture-of-experts setup. Clover currently has 8 full-sized experts per language layer with top-1 routing. The…