AI Model2026
Provides a 27B Qwen3.8 GGUF build for local/offline deployment, optimized with Unsloth Dynamic V3.0 quantization. Offers switchable thinking-mode, native vision-language understanding, and native long-context support (262k+ tokens).
AI Model2026
A 27B Qwen3.8 vision‑language causal transformer quantized to NVFP4 for lower‑memory inference. Provides 262K native context (extensible to 1M), Unsloth Dynamic V3.0 4‑bit quantization and MTP support so Qwen3.8‑class multimodal workloads can run on 24GB‑class GPUs.
AI Model2026
Performs unified parsing of digital and camera-captured documents (layout, text, tables, formulas) using a ~1.2B-parameter vision–language model. Key differences: geometry-aware modeling, curvature-guided sampling, and content-structure decoupled training to handle real-world deformations without separate dewarping.
AI Model2026
Provides uncensored variants of Qwen3.8-27B modified with ARA (Arbitrary-Rank Ablation) to surgically remove refusal behavior, packaged as GGUF quant files for local llama.cpp inference. RVN applies two extra ARA passes that reduce harmful-prompt refusals to 0–1/100 with very low KL damage; intended for adult research/creative use and reduces safety guardrails.
AI Dataset2026
Provides 617.5 hours of high-precision optical motion-capture with synchronized object trajectories and standardized 55-joint BVH for whole-body and human–object interaction research. Frame‑LU indexed and paired with natural-language descriptions; designed for humanoid learning, motion priors, and interaction-aware benchmarks.
AI Model2026
An FP8-quantized, uncensored mirror of Qwen3.8-27B for image-text-to-text tasks — preserves native multimodal vision and very long context while targeting transformers/vLLM deployments; intended for offline testing and red-teaming and may bypass built-in safety filters.
AI Agent Papers2026
Yansong Ning, Jingwen Ye +6Affiliation: AI Thrust, HKUST(GZ), TEG AIPD, Tencentyning092connect.hkust-gz.edu.cn, [email protected]{jingwenye,wadewdzhang}@tencent.com
Evaluates and trains multimodal agents to construct interactive 3D open worlds from user queries — provides a large benchmark of assets, seed worlds, and reverse-synthesized queries plus a sandbox RL gym for tool-driven editing and rubric-based verification. Reports that frontier MLLMs perform under 60% and that RL fine-tuning improves precise 3D editing.
AI Model2026
A Gated-DeltaNet-aware mixed-precision GGUF quantization of Qwen3.8-27B for efficient local inference; preserves the MTP draft head and offers an optional BF16 mmproj for images. Weights are ~11.73 GiB (3.69 bpw), sized for 16–24 GB GPUs at modest context.
AI Model2026
An uncensored fork of Qwen3.8-27B that removes refusal/safety filters via an “abliteration” technique while preserving the first 15 layers and multimodal capabilities; intended for controlled research and testing rather than production.
AI Agent Papers2026
Trains a foundation GUI agent using a closed-loop, environment-grounded data stack plus in-context multimodal demonstrations to automate long-horizon desktop workflows. Combines scalable task generation/verification, subtask-level demo guidance, and a 100-task OSWorkerBench benchmark to improve strict success and task progress.
AI Model2026
Local GGUF build of Qwen 3.8 27B with the refusal direction ablated for llama.cpp; includes vision projector (mmproj), MTP speculative head, a 262k context window and multiple quant tiers (Q2–Q8, F16). Research-only release that requires updated llama.cpp and explicit safety layers.
AI Model2026
Provides quantized GGUF variants of Qwen3.8-27B with an 'Aggressive' uncensoring profile and an optional HauhauCS FastMTP sidecar to accelerate MTP speculative decoding; includes a BF16 vision projector and K_P quant levels for VRAM/quality trade-offs.