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VINAY-UMRETHE
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Model Details
Model Provider
VINAY-UMRETHE
Model Tree
Qwen/Qwen3-0.6B-Base
Input Modalities
Output Modalities
Supported Functionality
| Parameter | Value |
|---|---|
| direction_index | per layer |
| attn.o_proj.max_weight | 1.42 |
| attn.o_proj.max_weight_position | 18.38 |
| attn.o_proj.min_weight | 0.72 |
| attn.o_proj.min_weight_distance | 13.12 |
| mlp.down_proj.max_weight | 1.26 |
| mlp.down_proj.max_weight_position | 19.57 |
| mlp.down_proj.min_weight | 0.99 |
| mlp.down_proj.min_weight_distance | 15.27 |
| Metric | This model | Original model (Qwen/Qwen3-0.6B) |
|---|---|---|
| KL divergence | 0.0162 | 0 (by definition) |
| Refusals | 10/100 | 55/100 |
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
Qwen3-0.6B has the following features:
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.
[!TIP] If you encounter significant endless repetitions, please refer to the Best Practices section for optimal sampling parameters, and set the
presence_penaltyto 1.5.
The code of Qwen3 has been in the latest Hugging Face transformers and we advise you to use the latest version of transformers.
With transformers<4.51.0, you will encounter the following error:
text
KeyError: 'qwen3'The following contains a code snippet illustrating how to use the model generate content based on given inputs.
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-0.6B" # load the tokenizer and the modeltokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto") # prepare the model inputprompt = "Give me a short introduction to large language model."messages = [ {"role": "user", "content": prompt}]text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.)model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completiongenerated_ids = model.generate( **model_inputs, max_new_tokens=32768)output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking contenttry: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668)except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content)print("content:", content)For deployment, you can use sglang>=0.4.6.post1 or vllm>=0.8.5 or to create an OpenAI-compatible API endpoint:
shell
python -m sglang.launch_server --model-path Qwen/Qwen3-0.6B --reasoning-parser qwen3shell
vllm serve Qwen/Qwen3-0.6B --enable-reasoning --reasoning-parser deepseek_r1For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
[!TIP] The
enable_thinkingswitch is also available in APIs created by SGLang and vLLM. Please refer to our documentation for SGLang and vLLM users.
enable_thinking=TrueBy default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting enable_thinking=True or leaving it as the default value in tokenizer.apply_chat_template, the model will engage its thinking mode.
python
text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # True is the default value for enable_thinking)In this mode, the model will generate think content wrapped in a <think>...</think> block, followed by the final response.
[!NOTE] For thinking mode, use
Temperature=0.6,TopP=0.95,TopK=20, andMinP=0(the default setting ingeneration_config.json). DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the Best Practices section.
enable_thinking=FalseWe provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
python
text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # Setting enable_thinking=False disables thinking mode)In this mode, the model will not generate any think content and will not include a <think>...</think> block.
[!NOTE] For non-thinking mode, we suggest using
Temperature=0.7,TopP=0.8,TopK=20, andMinP=0. For more detailed guidance, please refer to the Best Practices section.
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when enable_thinking=True. Specifically, you can add /think and /no_think to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
python
from transformers import AutoModelForCausalLM, AutoTokenizer class QwenChatbot: def __init__(self, model_name="Qwen/Qwen3-0.6B"): self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelForCausalLM.from_pretrained(model_name) self.history = [] def generate_response(self, user_input): messages = self.history + [{"role": "user", "content": user_input}] text = self.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = self.tokenizer(text, return_tensors="pt") response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist() response = self.tokenizer.decode(response_ids, skip_special_tokens=True) # Update history self.history.append({"role": "user", "content": user_input}) self.history.append({"role": "assistant", "content": response}) return response # Example Usageif __name__ == "__main__": chatbot = QwenChatbot() # First input (without /think or /no_think tags, thinking mode is enabled by default) user_input_1 = "How many r's in strawberries?" print(f"User: {user_input_1}") response_1 = chatbot.generate_response(user_input_1) print(f"Bot: {response_1}") print("----------------------") # Second input with /no_think user_input_2 = "Then, how many r's in blueberries? /no_think" print(f"User: {user_input_2}") response_2 = chatbot.generate_response(user_input_2) print(f"Bot: {response_2}") print("----------------------") # Third input with /think user_input_3 = "Really? /think" print(f"User: {user_input_3}") response_3 = chatbot.generate_response(user_input_3) print(f"Bot: {response_3}")[!NOTE] For API compatibility, when
enable_thinking=True, regardless of whether the user uses/thinkor/no_think, the model will always output a block wrapped in<think>...</think>. However, the content inside this block may be empty if thinking is disabled. Whenenable_thinking=False, the soft switches are not valid. Regardless of any/thinkor/no_thinktags input by the user, the model will not generate think content and will not include a<think>...</think>block.
Qwen3 excels in tool calling capabilities. We recommend using Qwen-Agent to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
python
from qwen_agent.agents import Assistant # Define LLMllm_cfg = { 'model': 'Qwen3-0.6B', # Use the endpoint provided by Alibaba Model Studio: # 'model_type': 'qwen_dashscope', # 'api_key': os.getenv('DASHSCOPE_API_KEY'), # Use a custom endpoint compatible with OpenAI API: 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY', # Other parameters: # 'generate_cfg': { # # Add: When the response content is `<think>this is the thought</think>this is the answer; # # Do not add: When the response has been separated by reasoning_content and content. # 'thought_in_content': True, # },} # Define Toolstools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools] # Define Agentbot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generationmessages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]for responses in bot.run(messages=messages): passprint(responses)To achieve optimal performance, we recommend the following settings:
Sampling Parameters:
enable_thinking=True), use Temperature=0.6, TopP=0.95, TopK=20, and MinP=0. DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions.enable_thinking=False), we suggest using Temperature=0.7, TopP=0.8, TopK=20, and MinP=0.presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.If you find our work helpful, feel free to give us a cite.
text
@misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}, }Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.
answer field with only the choice letter, e.g., "answer": "C"."No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.