Instructions to use SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF

SGLang

How to use SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF
```

APEX-I MTP GGUF Apache-2.0

Qwen-AgentWorld-35B-A3B-MTP-APEX

English | 中文 README

AgentWorld world model · 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 is the original Qwen release with no LoRA or abliteration applied. One native MTP layer from a compatible Qwen3.5-35B-A3B MTP shard was injected into the safetensors checkpoint, the result was converted to BF16 GGUF, and then quantized into APEX-I tiers using a model-specific imatrix.

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

MTP Extraction And Injection

The MTP layer was extracted from a compatible Qwen3.5-35B-A3B MTP shard and injected into the AgentWorld safetensors model.

Tensor prefix	`mtp.*`
Tensor count	785
Injected shard	`model-00022-of-00022.safetensors`

BenchLocal Results (I-Compact, thinking)

Model	TC	BF	HA	Ceiling	Practical	Retry
Qwen-AgentWorld-35B-A3B, thinking	100	87	81	88.5	87.5	1
Qwen3.6-35B thinking baseline	97	88	85	89.5	81.5	8

Thinking mode is strongly recommended for this model family.

Usage

llama.cpp server

hf download SC117/Qwen-AgentWorld-35B-A3B-MTP-APEX-GGUF --include "*.gguf" --local-dir ./models/qwen-agentworld-mtp-apex ./llama-server -m ./models/qwen-agentworld-mtp-apex/Qwen-AgentWorld-35B-A3B-MTP-APEX-I-Compact.gguf -ngl 99 -c 131072

vision + text

./llama-server -m ./models/qwen-agentworld-mtp-apex/Qwen-AgentWorld-35B-A3B-MTP-APEX-I-Compact.gguf --mmproj ./models/qwen-agentworld-mtp-apex/mmproj-f16.gguf -ngl 99 -c 131072

APEX Quantization Tiers

File	Size	Profile	Best For
`*-APEX-I-Balanced.gguf`	24.33 GB	I-Balanced	Highest-retention all-rounder
`*-APEX-I-Quality.gguf`	21.87 GB	I-Quality	Quality-focused, IQ4_XS middle experts
`*-APEX-I-Compact.gguf`	16.14 GB	I-Compact	Recommended quality/size ratio
`*-APEX-I-Mini.gguf`	13.29 GB	I-Mini	Smallest practical tier

Quantized with a model-specific imatrix generated from cleaned AgentWorld calibration text.

Citation

@misc{qwen-agentworld-2026,
  title = {Qwen-AgentWorld: Language World Models for General Agents},
  author = {Qwen Team},
  year = {2026},
  url = {https://github.com/QwenLM/Qwen-AgentWorld}
}

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GGUF

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0.4B params

Architecture

clip

Hardware compatibility

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