Chatbot2026
Provides a drop-in Jinja chat template for Qwen 3.5/3.6/3.8 that reduces reasoning-token waste, enforces a concise terseness system prompt, and preserves in-chat reasoning and tool-call rendering across turns. Terseness is on by default but switchable per request; no model weights are changed.
AI Model2026
Converts raw ASR transcripts into clean written text: adds punctuation and capitalization, expands spoken numbers/dates/times/currencies/emails, removes fillers and resolves self-corrections. Fine-tuned from Qwen3-0.6B (≈0.6B params), 94.8% token accuracy on a 7,519-case English test set; designed for CPU/edge deployment and deterministic post-processing.
AI Video Papers2026
Chuyue Li, Jinpeng Yu +8Affiliation: Qwen Business Unit of Alibaba, Affiliation: ShanghaiTech University +3
Encodes videos into a Film Knowledge Graph and reconstructs them to learn agent-native, editable video representations for agentic reasoning and manipulation. Uses agentic auto-encoding with dual-loop textual-gradient optimization, reports large reconstruction gains, and releases a benchmark and dataset.
AI Model2026
Provides an FP8-post-trained 27B multimodal causal language model with a native vision encoder, large-context support (262,144 native, extensible to 1,000,000), controllable thinking-mode reasoning, and compatibility with common inference engines for deployment.
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 Agent Papers2026
Defines "agentic transactions" and an ACID-style reliability framework for LLM agents that manage long-horizon tasks over persistent environments. Implements an ACID-compliant data agent using exploration–execution–validation cycles, confidence-divergence checks, semantic isolation, and append-only durable workspaces.
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 Model2026
Provides locally runnable GGUF quantizations of Qwen3.8-27B with the MTP speculative-draft head preserved and a Heretic weight edit that substantially reduces refusal rate. Ships multiple quant sizes with published imatrix and perplexity measurements for local inference under Apache‑2.0.
Separates knowledge storage (a global Memory) from iterative reasoning operators (multiple Reasoners) to improve knowledge compression and inference efficiency; reports a 7B model matching baseline with 62.6% of training data and a 35B Intern-S2-Mobius achieving ~4x end-to-end speedup.
AI Model2026
Provides a full GGUF quant ladder of an "abliterated" Qwen3.8-27B for local llama.cpp inference — includes every K-quant, embedded MTP speculative head, and optional vision projectors; refusal behavior was reduced at the weight level, so validate before production.
AI Model2026
A 9B-parameter distillation that transfers chain-of-thought reasoning from Qwen3.8 into the Qwen3.5-9B architecture for single‑GPU deployment; trained on ~70,000 teacher traces, it offers 262k-token context, native function-calling, and improved MMLU performance.