Qwen-AgentWorld-35B-A3B-GGUF
Qwen-AgentWorld-35B-A3B is a 35-billion-parameter language model specialized for simulating agent environments across seven domains: tool calling, search, terminal, software engineering, Android, web, and OS interaction. It uses a GGUF-quantized format for efficient deployment and is designed to predict environment state changes given agent actions, functioning as a 'world model' rather than a general-purpose chatbot. Licensed under Apache 2.0 with no gating restrictions.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | unsloth |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 377.3k |
| Likes | 156 |
| Last updated | 2026-06-25 |
| Source | unsloth/Qwen-AgentWorld-35B-A3B-GGUF |
What Qwen-AgentWorld-35B-A3B-GGUF is
Native language world model (Qwen3.5 base, 35B total parameters with 3B activated via MoE) trained through continual pre-training, supervised fine-tuning, and reinforcement learning (GSPO). Features 262,144-token context window, mixed Gated DeltaNet and attention layers, 256 MoE experts (8 routed + 1 shared), and is quantized in GGUF format by Unsloth. Comparable to vLLM and SGLang serving frameworks. No external API outputs in training.
Run Qwen-AgentWorld-35B-A3B-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="unsloth/Qwen-AgentWorld-35B-A3B-GGUF")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Minimum 8–16 GB VRAM per GPU for single-device inference (GGUF quantization reduces from ~70 GB full precision). For production serving with throughput: 4× A100/H100 or 8× RTX 6000 Ada recommended. Context length (262K tokens) requires proportional memory; reducing to 128K tokens saves ~50% memory. Tensor parallelism (tp-size 4 in examples) distributes load; estimate ~14 GB per GPU for tp-size=4 deployment.
Unknown. Card does not disclose LoRA/QLoRA compatibility, parameter count specifics for adapter training, or recommended fine-tuning frameworks. GGUF format may complicate direct fine-tuning workflows (typically requires conversion back to full precision or LoRA adapter layer integration). Recommend consulting Unsloth documentation or Qwen GitHub for adapter layer support.
When to avoid it — and what to weigh
- Real-Time, Single-Digit-Millisecond Inference — 35B parameters (even with MoE sparsity) require substantial compute. Unsloth GGUF format helps, but latency will exceed 100ms for typical hardware; not suitable for sub-100ms SLA workloads.
- Factual Knowledge Cutoff or Real-Time Information — Model is optimized for environment simulation, not knowledge retrieval. Will underperform on open-ended QA, current events, or domains where ground-truth facts matter more than plausible trajectory prediction.
- Fully Autonomous Deployment Without Verification — Model-generated predictions (terminal output, webpage states) are simulated and may diverge from ground truth. Requires human-in-the-loop validation or integration with real environment feedback for production agentic systems.
- Consumer-Grade or Severely Memory-Constrained Hardware — Even quantized, 35B requires minimum ~8–16 GB VRAM with tensor parallelism or dynamic batching. Estimate ~14 GB VRAM for single-GPU inference; recommend 4× H100 or equivalent for production throughput.
License & commercial use
Apache License 2.0 (apache-2.0). Permissive OSI-approved license allowing use, modification, and distribution with attribution. No restrictions on source form or derivative works. No viral copyleft.
Apache 2.0 is a permissive OSI license that explicitly permits commercial use, including for proprietary derivative works. You may build commercial products using Qwen-AgentWorld-35B-A3B, train on proprietary data, and redistribute. No royalties or special licensing required. No gating; model is publicly available. However, verify compliance with Qwen's parent org (Alibaba) usage policies and any downstream service terms (vLLM, SGLang, Hugging Face) if deploying via those platforms.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Card states 'No outputs from external API services included in training.' This mitigates data-poisoning risk. GGUF quantization is a binary format; verify checksum integrity when downloading. Model operates on local inputs and generates predictions; no telemetry or API callouts disclosed. If deploying via cloud (vLLM/SGLang API), apply standard API security (authentication, rate-limiting, input validation). Multi-turn reasoning with long context may increase token-injection risk in multi-user environments; isolate user contexts. No third-party security audit mentioned; recommend custom red-teaming for production agentic workflows.
Alternatives to consider
GPT-4o or Claude Opus 4
Closed-source alternatives with higher AgentWorldBench scores (58.25, 56.59 vs. 56.39 for 35B model). Trade off cost, API dependency, and commercial terms for stronger multi-domain performance and real-time fact access.
Qwen-AgentWorld-397B-A17B
Larger sibling (397B, 17B activated) from same family; 58.71 benchmark score vs. 56.39. Higher compute cost but stronger performance, especially in search and SWE. Same license and training approach.
DeepSeek-V4-Pro or other open-weight MoE models
Comparable parameter efficiency and open-source availability. Lower AgentWorldBench scores in world-model tasks but may offer stronger general reasoning. Evaluate trade-off between domain specialization and generality.
Ship Qwen-AgentWorld-35B-A3B-GGUF with senior software developers
Download Qwen-AgentWorld-35B-A3B GGUF from Hugging Face and deploy via vLLM or SGLang. Consult the technical report and GitHub repository for domain-specific system prompts and integration examples.
Talk to DEV.coRelated open-source tools
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Related on DEV.co
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Qwen-AgentWorld-35B-A3B-GGUF FAQ
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What GPU hardware do I need to run this model?
How does Qwen-AgentWorld compare to general-purpose LLMs like GPT or Claude?
Can I fine-tune this model?
Software developers & web developers for hire
Adopting Qwen-AgentWorld-35B-A3B-GGUF is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source llms software in production.
Start Simulating Agent Environments Today
Download Qwen-AgentWorld-35B-A3B GGUF from Hugging Face and deploy via vLLM or SGLang. Consult the technical report and GitHub repository for domain-specific system prompts and integration examples.