{"v":1,"id":"model:hf:unsloth/Qwen-AgentWorld-35B-A3B-GGUF","slug":"model-unsloth-qwen-agentworld-35b-a3b-gguf","kind":"model","category":"llm","title":"Qwen-AgentWorld-35B-A3B-GGUF","summary":"Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.","source":{"provider":"hf","ref":"unsloth/Qwen-AgentWorld-35B-A3B-GGUF","url":"https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF","rev":"3a305abf5cfd119ee999dfe929c433746edd8d63","fetchedAt":"2026-10-02T20:53:49.082Z","etag":"W/\"3df4-3xisV5XGDPTiG+XKhbzLvJb0gGw\""},"author":{"name":"unsloth","url":"https://huggingface.co/unsloth"},"license":{"spdx":"apache-2.0","raw":"apache-2.0","url":"https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B/blob/main/LICENSE","open":true},"metrics":{"downloads":353739,"downloadsWeek":327838,"likes":247,"stars":27665,"openIssues":68,"pushedAt":"2026-01-09T03:05:47Z","takenAt":"2026-10-02T20:53:49.082Z"},"tags":["transformers","gguf","qwen","unsloth","world-model","agent","environment-simulation","text-generation","dataset:Qwen/AgentWorldBench","endpoints_compatible","imatrix","conversational"],"pipeline":"text-generation","links":{"github":"QwenLM/Qwen3","npm":"node-llama-cpp"},"updatedAt":"2026-06-25T05:51:07.000Z","collectedAt":"2026-10-02T20:53:49.082Z","review":{"numbers":["353,739 downloads on Hugging Face","247 likes","license apache-2.0","21 GB for Qwen-AgentWorld-35B-A3B-UD-Q4_K_M.gguf","27,665 stars on QwenLM/Qwen3","68 open issues and PRs","327,838 npm downloads a week for node-llama-cpp","latest node-llama-cpp@3.22.1"],"log":null},"trust":"unlabeled","health":{"status":"alive","checkedAt":"2026-10-02T20:53:49.082Z","http":200},"description":"Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.\n  \n  \n    \n      \n    \n    \n      \n    \n    \n      \n    \n  \n\n# Qwen-AgentWorld-35B-A3B\n\n  \n  \n    📑 Technical Report |\n    📖 Blog |\n    🤗 Hugging Face |\n    🤖 ModelScope |\n    💻 GitHub |\n    🖥️ Demo\n  \n\n> [!Note]\n> This repository contains the model weights and configuration files for **Qwen-AgentWorld-35B-A3B**, a native language world model trained for agentic environment simulation.\n>\n> These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc.\n\n**Qwen-AgentWorld** is the first language world model to cover seven agent interaction domains within a single model. It simulates agentic environments via long chain-of-thought reasoning, predicting the next environment state given an agent's action and interaction history. Trained through a three-stage pipeline — CPT injects environment knowledge, SFT activates next-state-prediction reasoning, RL sharpens simulation fidelity — Qwen-AgentWorld is a **native world model**: environment modeling is the training objective from the CPT stage onward, not a post-hoc add-on.\n\n## Highlights\n\n- **Seven Unified Domains.** A single model covers MCP (tool calling), Search, Terminal, SWE (software engineering), Android, Web, and OS — spanning both text and GUI interaction environments.\n- **Native World Model.** Environment modeling from CPT onward, not post-hoc adaptation on a general-purpose LLM.\n- **Generalizable, Scalable & Controllable Simulator.** Zero-shot generalization to OOD environments (e.g., OpenClaw); controllable perturbations and fictional-world construction surpass real-environment training.\n- **Agent Foundation Model.** LWM RL warm-up on single-turn, non-agentic trajectories transfers to multi-turn, tool-calling agentic tasks across 7 benchmarks, including 3 entirely out-of-domain.\n\n## Model Overview\n\n- Type: Causal Language Model (Languag…\n\nSource: 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