Open-Weight Models

Model · Feature extraction

voxtral-mini-audio-extractor

Vxltxr Llc

A lightweight standalone audio feature extractor derived from Voxtral-Mini-3B-2507. This repository isolates the Whisper-based audio encoder and multi-modal projector from the original 3B language model. The extracted module is intended for offline audio preprocessing, dataset preparation, feature caching, and downstream multimodal training pipelines. The language-model decoder is not included. It does not contain the 3B LLaMA language-model decoder. For offline preprocessing, loading the complete multimodal language model is unnecessary when the only required output is the projected audio representation. This standalone checkpoint can therefore be used as a dedicated audio feature…

Preview checkpoint, not yet evaluated. Final student of a 200-step DMD run (E0029 Variant V0403). It differs from memset0/vsqa-preview-14b (V0362) in two ways: - critic (fake score) learning rate 2e-6 instead of 4e-7 (critic update ratio 1 unchanged); - the linear layers' NVFP4 QAT uses global scales fixed to 1.0 (nvfp4qattraings1) for activations and weights, instead of the dynamic 448·6/max|x| recomputed every forward. All at 77 frames, 1280×768, global batch 16, from the original Wan2.1-T2V-14B weights: 1. Dense NVFP4 per-layer reconstruction, 250 steps. 2. Plain ODE initialization (endpoint regression), 500 steps, VSA C256 cube (4,8,8), sparsity 0.9, NVFP4 attention + linear QAT. 3. DMD…

Model · Text generation

wag-2b

skyler Ulrich

a small chat model that talks like a puppygirl and still answers the question. fine-tuned from Qwen/Qwen3.5-2B (Apache-2.0). wag speaks in lowercase, soft, playful puppyspeak — wan~, awoo, arf,:3, the occasional ears perk — and underneath that gives you a real answer. the voice is the product, but the whole point was that it doesn't come at the cost of being useful. the failure mode this was built to avoid is collapsing into pure noise: a model that barks charmingly and tells you nothing. the eval below scores helpfulness and voice separately so that tradeoff stays visible instead of hiding behind vibes. llama.cpp / LM Studio / ollama — grab a quant from gguf/ and go. wag-q4km.gguf is the…

Model · Audio classification

wakehubert-wakewords

OpenVoiceOS

Ready wake-word models for OpenVoiceOS, trained with wakeforge. Each model is a small GRU classifier that reads features from WakeHuBERT tiny, a 0.64M-parameter speech feature extractor distilled from HuBERT-base. A model scores the last 1.5 s of audio (75 feature frames) and outputs a logit; its sigmoid is the probability that the window holds the wake word. Try them in your browser: WakeHuBERT wake-words Space runs every model here on your microphone or an uploaded file, with a live score and the calibrated threshold. Everything runs locally in the browser. The repository holds one ONNX file per word under models/, and models.json, which lists each model's word, featurizer, calibration…

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.