Valen-4B combines Qwen3.5-4B with a two-layer MLP-Mixer decision head. It predicts probability distributions for Choice, Noul (yes/no), and Score questions. A shared state can contain text, images, or video and serve multiple questions. Valen-4B 基于 Qwen3.5-4B 和两层 MLP-Mixer 决策头，支持图文、文本、视频，以及共享 state 的多题推理。 These weights include merged LoRA adapters. BF16 rounding can cause small probability differences from the original training checkpoints used for the reported benchmarks. The verified environment uses Python 3.10, PyTorch 2.6.0, Transformers 5.4.0, torchvision 0.21.0, Pillow, and av 16.1.0. Flash Attention 2 requires a compatible flash-attn installation. For a local copy, replace the…
Organization
Valen
Valen-Team
Models in Library9
Datasets in Library0
Models on Hugging Face10
Followers6
Compare the published evidence
This profile connects the 9 model and dataset records shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.
Published tasks: Image and text to text.
Reported licenses: apache-2.0. Open a record to review its specific license and conditions.
Compare model specifications, source dates, and hardware estimates on the individual records. Publisher identity verification is separate from a model's quality, deployment readiness, or suitability for your work.
Models
Qwen3.5 with a two-layer MLP-Mixer decision head. Supports text, images and videos, and returns Choice, Noul and Score decisions. Multiple questions can share one state encoding with execution="sharedstate". Trained on millions of samples. Install Python 3.10+, PyTorch, Transformers 5.4.0, torchvision, Pillow and av. Flash Attention 2 is optional with a compatible CUDA build. The model contains its tokenizer, processor and custom inference code; a separate base-model download is unnecessary. Use attnimplementation="flashattention2" for Flash Attention, or execution="question" for independent questions. Video defaults to 16 frames. Image/video request examples and training instructions are…
Qwen3.5 with a two-layer MLP-Mixer decision head. Supports text, images and videos, and returns Choice, Noul and Score decisions. Multiple questions can share one state encoding with execution="sharedstate". Trained on millions of samples. Install Python 3.10+, PyTorch, Transformers 5.4.0, torchvision, Pillow and av. Flash Attention 2 is optional with a compatible CUDA build. The model contains its tokenizer, processor and custom inference code; a separate base-model download is unnecessary. Use attnimplementation="flashattention2" for Flash Attention, or execution="question" for independent questions. Video defaults to 16 frames. Image/video request examples and training instructions are…
This is the ONNX version of Valen-Team/Valen-0.8B. Qwen3.5 with a two-layer MLP-Mixer decision head. Supports text, images and videos, and returns Choice, Noul and Score decisions. Multiple questions can share one state encoding with execution="sharedstate". Trained on millions of samples. Install Python 3.10+, PyTorch, Transformers 5.4.0, torchvision, Pillow and av. Flash Attention 2 is optional with a compatible CUDA build. The model contains its tokenizer, processor and custom inference code; a separate base-model download is unnecessary. Use attnimplementation="flashattention2" for Flash Attention, or execution="question" for independent questions. Video defaults to 16 frames.…