Instructions to use groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4 with Transformers:

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

pipe = pipeline("text-generation", model="groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4")
model = AutoModelForMultimodalLM.from_pretrained("groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4

SGLang

How to use groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4 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 "groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4 with Docker Model Runner:
```
docker model run hf.co/groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4
```

Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4

A compact 4-bit GPTQ-Pro quantization of Qwen-AgentWorld-35B-A3B — built for local agent simulation, tool-use reasoning, terminal/world-state prediction, and long-context agent workflows.

This repository contains an INT4 GPTQ-Pro quantized version of Qwen/Qwen-AgentWorld-35B-A3B, preserving the core architecture and behavior of the original language world model while dramatically reducing the weight footprint for practical local serving.

The upstream model is not just another chat model with a tool-use sticker slapped on it. Qwen-AgentWorld is trained as a language world model: given an agent’s action and interaction history, it predicts the next environment observation/state. That makes it especially interesting for agentic workflows where the model needs to reason about what should happen next inside tools, terminals, browsers, operating systems, software projects, and simulated environments.

This quantized build is intended for people who want to actually run the thing, not frame the BF16 checkpoint and admire it from across the room.

What this model is good at

This model is best suited for:

Agentic environment simulation
Tool-call and MCP-style interaction modeling
Terminal state prediction
Software-engineering workflow simulation
Browser / web / OS interaction reasoning
Synthetic trajectory generation for agents
Long-context planning and replay
Local agent research
OpenAI-compatible backend serving

The upstream AgentWorld model covers seven major interaction domains:

Domain	Use case
MCP / Tool calling	Predicting tool results and interaction flow
Search	Simulating search-based information environments
Terminal	Predicting shell command outputs and filesystem-like state
SWE	Software-engineering task environments
Android	Mobile GUI interaction simulation
Web	Browser and website interaction simulation
OS	Desktop / operating-system style workflows

Model details

Field	Value
Base model	`Qwen/Qwen-AgentWorld-35B-A3B`
Quantized model	`groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4`
Architecture	Qwen3.5 MoE / Language World Model
Total parameters	35B
Active parameters	~3B active per token
Quantization	GPTQ-Pro INT4
Format	Safetensors
Context length	Up to 262,144 tokens, depending on serving stack and VRAM
License	Apache-2.0, following upstream

Why this quant exists

The original BF16 checkpoint is powerful, but heavy. This GPTQ-Pro INT4 build is aimed at practical deployment where you want the AgentWorld behavior without needing a datacenter-class inference box.

Use this when you want:

Smaller disk and VRAM footprint
Faster local experimentation
OpenAI-compatible serving
Multi-GPU consumer inference
Agent simulation without calling external APIs
A world-model backend for agent training, evaluation, or synthetic rollout generation

Quantization is not magic. INT4 can affect fidelity, especially for very long-context workloads. For serious benchmark or production use, compare against the BF16 upstream model on your own tasks.

Recommended serving: vLLM

Use a recent vLLM build. Qwen3.5 MoE support is new enough that old wheels may fail or silently behave like garbage. Delightful hobby, this one.

pip install -U vllm

Downloads last month: 208

Safetensors

Model size

35B params

Tensor type

BF16

I32

Model tree for groxaxo/Qwen-AgentWorld-35B-A3B-GPTQ-Pro-Int4

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen-AgentWorld-35B-A3B

Quantized

(73)

this model