Qwen AgentWorld 35B A3B
Qwen AgentWorld 35B A3B from Qwen. Source-based hardware guidance from its published configuration.
At a glance
- Parameters
- 34.66B
- Architecture
- qwen3_5_moe
- License
- apache-2.0
- Software
- SGLang, vLLM, Transformers
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Add my hardwareWays to run it
SGLang · Linux
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
python -m sglang.launch_server --model-path Qwen/Qwen-AgentWorld-35B-A3B --port 8000 --tp-size 4 --context-length 262144 --reasoning-parser qwen3vLLM · Linux
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
vllm serve Qwen/Qwen-AgentWorld-35B-A3B --port 8000 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --language-model-only --trust-remote-codeTransformers · See official guide
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen-AgentWorld-35B-A3B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto",
)
messages = [
{
"role": "system",
"content": "You are a language world model simulating a Linux terminal environment. "
"Given the user's command, predict the terminal output."
},
{
"role": "user",
"content": "Action: execute_bash\nCommand: ls -la /home/user/project/"
}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.6)
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
print(response)Explore its uses
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