AgentWorld world model · Abliterix uncensored · native MTP injected · APEX-I GGUFs
Important Warning
Qwen-AgentWorld is designed as an agentic environment simulation / language world model. It is intended to simulate agent-environment interactions and predict environment states from actions and history. It is not recommended as a general daily assistant, coding model, or autonomous Agent backbone unless you specifically need this world-model behavior.
About This Release
This package contains GGUF releases of Qwen-AgentWorld-35B-A3B, a Qwen3.5 MoE world model for agentic environment simulation.
The model was modified with an Abliterix uncensored LoRA, stream-merged into safetensors, injected with one native MTP layer from a compatible Qwen3.5-35B-A3B MTP shard, converted to BF16 GGUF, and quantized into APEX-I tiers using a model-specific imatrix.
The release keeps the BF16 GGUF, imatrix, and mmproj-F16.gguf for reproducibility and multimodal llama.cpp use.
Model Details
Base model
Qwen/Qwen-AgentWorld-35B-A3B
Architecture
Qwen3.5 MoE
Parameters
35B total, around 3B active per token
Experts
256 routed experts, 8 active per token
Context
262,144 tokens
MTP
1 injected MTP layer, 785 tensors
License
Apache-2.0
Abliterix Build
The uncensored variant was produced with Abliterix. The selected search result was trial 54 out of 110.
KL divergence
0.0163
Response length deviation
0.04 std devs
Refusals
21 / 100
The exported Abliterix LoRA was stream-merged into the full safetensors model before MTP injection and GGUF conversion.
MTP Extraction And Injection
The MTP layer was extracted from a compatible Qwen3.5-35B-A3B MTP shard and injected into the uncensored AgentWorld safetensors model.
Tensor prefix
mtp.*
Tensor count
785
Injected shard
model-00022-of-00022.safetensors
BenchLocal Results
Model
TC
BF
HA
Ceiling
Practical
Retry
Original, thinking
100
87
81
88.5
87.5
1
Uncensored + MTP, thinking
93
87
79
85.6
81.6
4
Uncensored + MTP, no thinking
83
83
63
75.0
75.0
0
Qwen3.6-35B thinking baseline
97
88
85
89.5
81.5
8
Thinking mode is strongly recommended for this model family.
1@misc{qwen-agentworld-2026,
2 title = {Qwen-AgentWorld: Language World Models for General Agents},
3 author = {Qwen Team},
4 year = {2026},
5 url = {https://github.com/QwenLM/Qwen-AgentWorld}
6}
