Qwen3
Datasets
All datasets matching “Qwen3”behaviour1k-Qwen3-features
BEHAVIOR-1K Qwen3 skill features
Per-frame conditioned features e_t = Phi(f_t, L_sub^(j), L), mean-pooled primitive skill latents S_j, aligned proprioception q_t, actions a_t, and subtask progress p_t.
These are the inputs and targets for a Primitive Skill Composer VLA Skill Predictor.
Ground-truth primitives come from BEHAVIOR-1K's hand-authored
primitive_annotation, so the segmentation is human-labelled rather than
predicted, and nothing here depends on a keyframe detector.… See the full description on the dataset page: https://huggingface.co/datasets/erl-hub/behaviour1k-Qwen3-features.Qwen3.8-27B-GGUF-metrics
Qwen3.8-27B GGUF, everything behind the numbers
This is the working record for
AtomicChat/Qwen3.8-27B-GGUF.
Every figure in that model card came from a file in here, including the ones
about other publishers' builds.
The point of publishing it is simple. A quantization comparison is only worth
reading if someone else can run it, and that needs three things nobody usually
ships: the exact reference the numbers were measured against, the exact text
they were measured on, and the… See the full description on the dataset page: https://huggingface.co/datasets/AtomicChat/Qwen3.8-27B-GGUF-metrics.Transmem_ecsd_qwen3_8b_hotpotqa_n4_n8Transmem_ecsd_qwen3_4b_hotpotqa_n4Qwen3.5-4B-Base
juiceb0xc0de/Qwen3.5-4B-Base
A brain atlas for Qwen/Qwen3.5-4B-Base, a 32-layer hybrid that runs linear attention on 24 layers and full attention on the other 8. This is not a chat dataset or a benchmark. It is an internal-mechanics map built by running activations through a corpus of prompts and scoring what each layer, component, head, and feature direction is doing.
This is a base model, before any instruction tuning, so whatever structure shows up here was put there by… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/Qwen3.5-4B-Base.qwen3.5-9b-atlas
qwen3.5-9b-atlas