Forcing qwen3-8b to only output the final answer in QA tasks

[qsuzer]

How can I prevent Qwen3-8B from outputting the chain-of-thought/reasoning process and force it to only output the final answer in QA tasks?
I set "enable_thinking" to False. However, it still output "Okay, let me ..."

[tahamajs]

Based on your description, it is highly likely you are using a "Thinking" variant (e.g., Qwen3-8B-Thinking or a similar reasoning-heavy fine-tune) rather than the standard Instruct model.

In these specific reasoning models, the "Chain of Thought" (CoT) is often baked into the model's training and forced by the chat template (e.g., automatically injecting a token). Setting enable_thinking=False often fails because the model is fundamentally designed to "think" before answering; suppressing this process usually degrades the answer quality significantly or causes the model to hallucinate.

Here are the three methods to solve this, ranked from most effective to least effective.

1. The Best Fix: Switch to the "Instruct" Variant
   If you strictly want a final answer without the reasoning overhead (latency/tokens), you are using the wrong tool for the job. "Thinking" models must think to perform well.

Action: Switch your model path from Qwen/Qwen3-8B-Thinking to Qwen/Qwen3-8B-Instruct.

Why: The Instruct version is trained to respond directly and obeys standard system prompts much better. It is designed for standard QA tasks where you want a direct answer.

2. The Parsing Fix: Strip the Thoughts (Post-Processing)
   If you must use the reasoning model (perhaps for its higher intelligence) but just want to display the final answer, do not try to stop the generation. Let it think, but hide the thought process programmatically.

The model typically wraps reasoning in specific tags (often ... or standard delimiters).

Python Example (Regex):

import re
response_text = "Okay, let me think... [Reasoning process] ... The answer is 42."
clean_answer = re.sub(r'.*?', '', response_text, flags=re.DOTALL).strip()

print(clean_answer)

3. The Prompt/Config Fix (Attempt to force it)
   If you cannot switch models or post-process, you can try to brute-force the model into compliance, though this is often unreliable with reasoning models.

Modify the System Prompt: Reasoning models often ignore standard "be concise" prompts. You need to be aggressive and specifically forbid the "inner monologue" style.

SYSTEM: You are a helpful assistant. You must output ONLY the final answer. Do not use any internal monologue, reasoning, or phrases like "Okay, let me think". Go directly to the answer.
Inject a Pre-filled Assistant Response (Prefill): Force the model to skip the "Okay, let me..." preamble by pre-filling the start of its response.

Input: User: What is 2+2?
Pre-filled Assistant: The answer is
Model Completion: 4.

Note: This requires an inference backend that supports "pre-filling" the assistant role (like llama.cpp or some API endpoints).