Instructions to use mlx-community/Qwen-AgentWorld-35B-A3B-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Qwen-AgentWorld-35B-A3B-bf16 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Qwen-AgentWorld-35B-A3B-bf16")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use mlx-community/Qwen-AgentWorld-35B-A3B-bf16 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Qwen-AgentWorld-35B-A3B-bf16"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mlx-community/Qwen-AgentWorld-35B-A3B-bf16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/Qwen-AgentWorld-35B-A3B-bf16 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Qwen-AgentWorld-35B-A3B-bf16"

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 mlx-community/Qwen-AgentWorld-35B-A3B-bf16

Run Hermes

hermes

MLX LM

How to use mlx-community/Qwen-AgentWorld-35B-A3B-bf16 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/Qwen-AgentWorld-35B-A3B-bf16"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/Qwen-AgentWorld-35B-A3B-bf16"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/Qwen-AgentWorld-35B-A3B-bf16",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen-AgentWorld-35B-A3B-bf16

Full-precision (bfloat16) MLX build of Qwen/Qwen-AgentWorld-35B-A3B. The original weights load in mlx-lm / oMLX as-is — this repo packages them for Apple Silicon without any precision loss.

For Apple Silicon. Runs in mlx-lm, oMLX, or any MLX app. Quantized siblings: oQ4 (≈4.6 bpw, ≈19 GB) and oQ3.5 (≈3.5 bpw, ≈16 GB).

Notes

Text-only. The checkpoint declares a vision_config and MTP heads in config.json, but ships no vision or mtp.* weights (693 tensors, 0 vision, 0 MTP) — both are vestigial skeleton inherited from the Qwen3.5 base, and language_model_only: true makes it a pure LM. Nothing multimodal is present to use.
≈65 GB on disk; peak memory ≈69 GB generating. Fits in 128 GB unified memory; use a quantized sibling on smaller machines.

Performance

Measured with oMLX (Auto engine) on a Macbook Pro M5 Max 128GB 40 GPU. Single request:

Context (pp/tg)	TTFT	decode	prefill	peak mem
1024 / 128	644 ms	77 tok/s	1591 tok/s	65.6 GB
4096 / 128	1.68 s	76 tok/s	2434 tok/s	66.4 GB
8192 / 128	2.39 s	75 tok/s	3428 tok/s	66.7 GB
32768 / 128	12.0 s	67 tok/s	2730 tok/s	68.7 GB

For ≈1.8× the single-request decode throughput at ≈⅓ the memory, see the oQ4 sibling.

Usage

mlx_lm.generate --model mlx-community/Qwen-AgentWorld-35B-A3B-bf16 \
  --system-prompt "You are a language world model simulating a Linux terminal. Given the user's command, predict the terminal output." \
  --prompt $'Action: execute_bash\nCommand: ls -la /home/user/project/' \
  --max-tokens 512 --temp 0.6

The model uses thinking mode (<think>...</think>) by default. Recommended sampling: temperature=0.6, top_p=0.95, top_k=20. See the base model card for the seven agent domains and domain-specific system prompts.

Downloads last month: 44

Safetensors

Model size

35B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

Model tree for mlx-community/Qwen-AgentWorld-35B-A3B-bf16

Base model

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

Finetuned

Qwen/Qwen-AgentWorld-35B-A3B

Finetuned

(14)

this model