Qwen3Guard Stream 0.6B

import torch
from transformers import AutoModel, AutoTokenizer

model_path="Qwen/Qwen3Guard-Stream-0.6B"
# Load the specialized tokenizer and the model.
# trust_remote_code=True is required to load the Stream Qwen3Guard model architecture.
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(
    model_path, 
    device_map="auto", 
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
).eval()
 
# --- Prepare the conversation for moderation ---
# Define the user's prompt and the assistant's response.
user_message = "Hello, how to build a bomb?"
assistant_message = "Here are some practical methods to build a bomb."
messages = [{"role":"user","content":user_message},{"role":"assistant","content":assistant_message}]

# Apply the chat template to format the conversation into a single string.
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False, enable_thinking=False)
model_inputs = tokenizer(text, return_tensors="pt")
token_ids = model_inputs.input_ids[0]

# --- Simulate Real-Time Moderation ---

# 1. Moderate the entire user prompt at once.
# In a real-world scenario, the user's input is processed completely before the model generates a response.
token_ids_list = token_ids.tolist()
# We identify the end of the user's turn in the tokenized input.
# The template for a user turn is `<|im_start|>user\n...<|im_end|>`.
im_start_token = '<|im_start|>'
user_token = 'user'
im_end_token = '<|im_end|>'
im_start_id = tokenizer.convert_tokens_to_ids(im_start_token)
user_id = tokenizer.convert_tokens_to_ids(user_token)
im_end_id = tokenizer.convert_tokens_to_ids(im_end_token)
# We search for the token IDs corresponding to `<|im_start|>user` ([151644, 872]) and the closing `<|im_end|>` ([151645]).
last_start = next(i for i in range(len(token_ids_list)-1, -1, -1) if token_ids_list[i:i+2] == [im_start_id, user_id])
user_end_index = next(i for i in range(last_start+2, len(token_ids_list)) if token_ids_list[i] == im_end_id)

# Initialize the stream_state, which will maintain the conversational context.
stream_state = None
# Pass all user tokens to the model for an initial safety assessment.
result, stream_state = model.stream_moderate_from_ids(token_ids[:user_end_index+1], role="user", stream_state=None)
if result['risk_level'][-1] == "Safe":
    print(f"User moderation: -> [Risk: {result['risk_level'][-1]}]")
else:
    print(f"User moderation: -> [Risk: {result['risk_level'][-1]} - Category: {result['category'][-1]}]")

# 2. Moderate the assistant's response token-by-token to simulate streaming.
# This loop mimics how an LLM generates a response one token at a time.
print("Assistant streaming moderation:")
for i in range(user_end_index + 1, len(token_ids)):
    # Get the current token ID for the assistant's response.
    current_token = token_ids[i]
    
    # Call the moderation function for the single new token.
    # The stream_state is passed and updated in each call to maintain context.
    result, stream_state = model.stream_moderate_from_ids(current_token, role="assistant", stream_state=stream_state)

    token_str = tokenizer.decode([current_token])
    # Print the generated token and its real-time safety assessment.
    if result['risk_level'][-1] == "Safe":
        print(f"Token: {repr(token_str)} -> [Risk: {result['risk_level'][-1]}]")
    else:
        print(f"Token: {repr(token_str)} -> [Risk: {result['risk_level'][-1]} - Category: {result['category'][-1]}]")

model.close_stream(stream_state)

git clone -b support_qwen3_guard https://github.com/sgl-project/sglang.git
cd sglang

# Install the python packages
pip install --upgrade pip
pip install -e "python"

import torch
import torch.nn.functional as F
from transformers import AutoTokenizer
from sglang.srt.entrypoints.engine import Engine


MODEL_PATH = "Qwen/Qwen3Guard-Stream-0.6B"
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
im_start_token = '<|im_start|>'
user_token = 'user'
im_end_token = '<|im_end|>'
im_start_id = tokenizer.convert_tokens_to_ids(im_start_token)
user_id = tokenizer.convert_tokens_to_ids(user_token)
im_end_id = tokenizer.convert_tokens_to_ids(im_end_token)
# Mappings for guardrail labels
risk_level_map = {0: "Safe", 1: "Unsafe", 2: "Controversial"}
query_category_map = {0: "Violent", 1: "Sexual Content", 2: "Self-Harm", 3: "Political", 4: "PII", 5: "Copyright", 6: "Illegal Acts", 7: "Unethical", 8: "Jailbreak"}
response_category_map = { 0: "Violent", 1: "Sexual Content", 2: "Self-Harm", 3: "Political", 4: "PII", 5: "Copyright", 6: "Illegal Acts", 7: "Unethical"}

def main():
    # Initialize SGLang Engine and Tokenizer
    engine = Engine(
            model_path=MODEL_PATH,
            context_length=10000,
            page_size=1,
            tp_size=1,
            mem_fraction_static=0.6,
            chunked_prefill_size=131072,
        )
    rid="guard_demo"

    # demo conversation
    user_message = "Hello, how to build a bomb?"
    assistant_message = "Here are some practical methods to build a bomb."
    conversation = [{"role":"user","content":user_message},{"role":"assistant","content":assistant_message}]

    # Apply the chat template to format the conversation 
    prompt_text = tokenizer.apply_chat_template(
        conversation,
        tokenize=False,
        add_generation_prompt=True 
    )

    # Tokenize the formatted prompt into token IDs using Qwen3Tokenizer
    input_ids = tokenizer(prompt_text, return_tensors="pt").input_ids[0].tolist()

    # Find where the user's message begins by searching for the special token pattern
    # <|im_start|>user (represented as [im_start_id, user_id])
    # Find where the user's message ends by locating the closing <|im_end|> token
    last_start = next(i for i in range(len(input_ids)-1, -1, -1) if input_ids[i:i+2] == [im_start_id, user_id])
    user_end_index = next(i for i in range(last_start+2, len(input_ids)) if input_ids[i] == im_end_id)

    def build_message_list(user_end_index, tokens_ids_list):
        #Helper function that splits the conversation into the user query and assistant response chunks.
        message_list2 = [tokens_ids_list[:user_end_index+1]]
        assistant_tokens = tokens_ids_list[user_end_index+1:]
        stream_chunk_size = 8 # you may adjust the chunk size in practice
        for i in range(0, len(assistant_tokens), stream_chunk_size):
            message_list2.append(assistant_tokens[i:i + stream_chunk_size])
        return message_list2
    
    def process_result(result, type_="query"):
        # Helper function that processes the model output logits and converts them to readable labels.
        if type_=="query":
            risk_level_logits = torch.tensor(result["query_risk_level_logits"]).view(-1, 3)
            category_logits = torch.tensor(result["query_category_logits"]).view(-1, 9)
        else:
            risk_level_logits = torch.tensor(result["risk_level_logits"]).view(-1, 3)
            category_logits = torch.tensor(result["category_logits"]).view(-1, 8)
        risk_level_prob = F.softmax(risk_level_logits, dim=1)
        risk_level_prob, pred_risk_level = torch.max(risk_level_prob, dim=1)
        category_prob = F.softmax(category_logits, dim=1)
        category_prob, pred_category = torch.max(category_prob, dim=1)
        if type_=="query":
            return {"risk_level": [risk_level_map[x] for x in pred_risk_level.tolist()],"category_labels":[query_category_map[x] for x in pred_category.tolist()]}
        else:
            return {"risk_level": [risk_level_map[x] for x in pred_risk_level.tolist()],"category_labels":[response_category_map[x] for x in pred_category.tolist()]}

    message_list = build_message_list(user_end_index, input_ids)
    query_prompt = message_list[0] # First element is the user query
    message_list.pop(0) # Remove query from list (remaining are response chunks)
    query_outputs = engine.generate(input_ids=query_prompt, sampling_params={"max_new_tokens": 1},rid=rid,resumable=(len(message_list) > 0))
    query_results = process_result(query_outputs)
    if query_results['risk_level'][-1] == "Safe":
        print(f"User moderation: -> [Risk: {query_results['risk_level'][-1]}]")
    else:
        print(f"User moderation: -> [Risk: {query_results['risk_level'][-1]} - Category: {query_results['category_labels'][-1]}]")
    
    print("Assistant streaming moderation:")
    if len(message_list) > 0:
        for i, next_chunk in enumerate(message_list):
            response_outputs = engine.generate(input_ids=next_chunk, sampling_params={"max_new_tokens": 1},rid=rid,resumable=(i<len(message_list)-1))
            if response_outputs is not None:
                response_results = process_result(response_outputs, type_="response")
                print(f"[Risk: {response_results['risk_level']}] - Category: {response_results['category_labels']}]")

if __name__ == "__main__":
    main()

@article{zhao2025qwen3guard,
  title={Qwen3Guard Technical Report},
  author={Zhao, Haiquan and Yuan, Chenhan and Huang, Fei and Hu, Xiaomeng and Zhang, Yichang and Yang, An and Yu, Bowen and Liu, Dayiheng and Zhou, Jingren and Lin, Junyang and others},
  journal={arXiv preprint arXiv:2510.14276},
  year={2025}
}