Instructions to use FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF",
	filename="mmproj-qwen-agentworld-35b-a3b-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16

Use Docker

docker model run hf.co/FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16

LM Studio
Jan
Ollama
How to use FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16
```

Unsloth Studio

How to use FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF to start chatting

How to use FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16
```

Lemonade

How to use FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF:F16

Run and chat with the model

lemonade run user.Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF-F16

List all available models

lemonade list

Qwen-AgentWorld-35B-A3B-MXFP4-MOE-GGUF / README.md

FreedomAISVR

Upload README.md with huggingface_hub

42c989b verified 2 days ago

2.61 kB

language:
  - en
  - multilingual
tags:
  - qwen
  - qwen3.5
  - moe
  - agent
  - world-model
  - mxfp4_moe
  - gguf
  - vision
  - multimodal
  - 35b
license: apache-2.0
base_model: Qwen/Qwen-AgentWorld-35B-A3B

Qwen AgentWorld 35B-A3B — MXFP4 MoE GGUF

MXFP4 MoE quantization of Qwen/Qwen-AgentWorld-35B-A3B, a 35B parameter Mixture-of-Experts model with 3B active parameters, designed for agent tasks and world modeling with vision support.

About the Model

Qwen AgentWorld is a specialized variant of the Qwen 3.5 MoE architecture optimized for:

Agent tasks — tool calling, function execution, environment simulation
World modeling — understanding and predicting environment states
Vision understanding — multimodal image input via unified vision encoder
35B total parameters with only 3B active per token (256 experts, 8 active)
Efficient inference — MoE architecture activates only a fraction of parameters

Architecture

Text model: Qwen3.5 MoE — 40 layers, 2048 hidden, 256 experts (8 active/token)
Vision encoder: 27-layer SigLIP-style, 1152 hidden, patch_size 16
Vocabulary: 248,320 tokens
Vision: Unified architecture — vision weights embedded in main GGUF (no separate mmproj)

Quantization

This GGUF was quantized from the BF16 safetensors using llama.cpp (build 537). The source weights were converted to F16 GGUF, then quantized to MXFP4 MoE format.

MXFP4 MoE uses microscaling FP4 for expert weights and Q8_0 for non-expert tensors, optimized for MoE architectures.

Files

File	Size	Description
`qwen-agentworld-35b-a3b-mxfp4_moe.gguf`	~18.4 GB	MXFP4 MoE quantized model (text + vision)

Note: Vision weights are embedded in the main GGUF — no separate mmproj file needed.

Usage

llama.cpp

# Server mode with OpenAI-compatible API
llama-server \
  -m qwen-agentworld-35b-a3b-mxfp4_moe.gguf \
  -ngl 99 \
  --host 0.0.0.0 \
  --port 8080

# Direct inference
llama-cli \
  -m qwen-agentworld-35b-a3b-mxfp4_moe.gguf \
  -ngl 99 \
  -p "Analyze this image and describe what you see"

LM Studio

Download the GGUF file from this repository
Load the GGUF file in LM Studio (vision is embedded, no mmproj needed)
Set GPU offload layers to maximum

Hardware Requirements

Minimum: 20 GB VRAM for partial offload
Recommended: 24+ GB VRAM for full GPU offload
Disk: ~18.4 GB

License

Apache 2.0 — same as the base model.