How do I get <think> from e.g. Qwen 3 prompt with /think adds a paragraph with think.

[LaKanDoR]

How do I get from e.g. Qwen3 prompt with /think adds a paragraph with reasoning.
howto activate --jininx --chat-template-file "xxx"?

https://qwen.readthedocs.io/en/latest/

Test ...

llm = Llama(
    model_path=r"C:\AI LLMS\gemma-3-4B-it-QAT-Q4_0.gguf",
    chat_format="mistral-instruct",
    n_batch=512,
    n_predict=128,
    n_ctx=4096,
    n_threads=8,
    n_gpu_layers=10,
)

output = llm.create_chat_completion(
    messages=[
        { "role": "system", "content": "/think You are a story writing assistant." },
        {
            "role": "user",
            "content": "Write a story about llamas."
        }
    ],
    stream=True
)

for chunk in output:
    print(chunk)
    if chunk == '[DONE]':
        print("Stream completed")
        break
    if not chunk or 'choices' not in chunk or not chunk['choices']:
        print("No valid response received from the server")
        continue
    # Process the chunk
    delta = chunk['choices'][0]['delta']    
    if 'role' in delta:
        print(delta['role'], end=': ')
    elif 'content' in delta:
        print(delta['content'], end='')

[LaKanDoR]

this is working with OpenAI an LM Studio

from openai import OpenAI

client = OpenAI(base_url='http://localhost:12345/v1', api_key='na')

# Use the following func to get the available models
# model_list = client.models.list()
# print(model_list)

chat_completion = client.chat.completions.create(
   model="C:\\AI LLMS\\gemma-3-4B-it-QAT-Q4_0.gguf",
   messages=[
      {
            "role": "user",
            "content": "Tell me something about large language models."
      }
   ],
   stream=True,
)
thinking_buf = ""
generation_buf = ""
in_think = False
for chunk in chat_completion:
      #print(chunk.choices[0].delta.content or "", end="")
      
      data = chunk.choices[0].delta.content or ""
  
      # Erkennung, ob wir gerade im Think-Block sind
      if "<think>" in data:
            in_think = True
            data = data.split("<think>")[1]
            
            print(f"(🧠 Beginne Denken...) {data}", end="", flush=True)
      if "</think>" in data:
            thinking_buf += data.split("</think>")[0]
            in_think = False
            data = data.split("</think>")[1]
            print(f"(🧠 Denken beenden...) {data}", end="", flush=True)
      if in_think:
            thinking_buf += data
            print(f"{data}", end="", flush=True)
      else:
            generation_buf += data
            print(data, end="", flush=True)

      if data:
            thinking_buf += data