Qwen3 1.7B FP8

🚀 Qwen3-1.7B-FP8

Qwen3-1.7B-FP8 is an advanced large language model offering high - performance text generation with unique features and excellent capabilities.

🚀 Quick Start

The code of Qwen3 has been integrated into the latest Hugging Face transformers. We strongly recommend using the latest version of transformers.

If you use transformers<4.51.0, you'll encounter the following error:

KeyError: 'qwen3'

Here is a code snippet demonstrating how to use the model to generate content based on given inputs:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen3-1.7B-FP8"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "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 completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

# parsing thinking content
try:
    # 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 to create an OpenAI - compatible API endpoint:

• SGLang:

  python -m sglang.launch_server --model-path Qwen/Qwen3-1.7B-FP8 --reasoning-parser qwen3
  

• vLLM:

  vllm serve Qwen/Qwen3-1.7B-FP8 --enable-reasoning --reasoning-parser deepseek_r1
  

For local use, applications such as Ollama, LMStudio, MLX - LM, llama.cpp, and KTransformers have also supported Qwen3.

✨ Features

Qwen3 Highlights

Qwen3 is the latest generation of large language models in the Qwen series, offering a comprehensive suite of dense and mixture - of - experts (MoE) models. After extensive training, Qwen3 brings groundbreaking advancements in reasoning, instruction - following, agent capabilities, and multilingual support, with the following key features:

• Unique seamless switching: It supports seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non - thinking mode (for efficient, general - purpose dialogue) within a single model, ensuring optimal performance across various scenarios.
• Enhanced reasoning capabilities: It has significantly improved reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non - thinking mode) in mathematics, code generation, and commonsense logical reasoning.
• Superior human preference alignment: It excels in creative writing, role - playing, multi - turn dialogues, and instruction following, providing a more natural, engaging, and immersive conversational experience.
• Expert agent capabilities: It enables precise integration with external tools in both thinking and non - thinking modes and achieves leading performance among open - source models in complex agent - based tasks.
• Multilingual support: It supports 100+ languages and dialects with strong capabilities for multilingual instruction following and translation.

📦 Installation

Since the code of Qwen3 is in the latest Hugging Face transformers, you just need to ensure you are using the latest version of transformers.

💻 Usage Examples

Basic Usage

The above Quick Start section already provides a basic usage example of using Qwen3 - 1.7B - FP8 to generate text.

Advanced Usage: Switching Between Thinking and Non - Thinking Modes via User Input

We offer 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:

from transformers import AutoModelForCausalLM, AutoTokenizer

class QwenChatbot:
    def __init__(self, model_name="Qwen/Qwen3-1.7B-FP8"):
        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 Usage
if __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}")

Agentic Use

Qwen3 excels in tool - calling capabilities. We recommend using [Qwen - Agent](https://github.com/QwenLM/Qwen - Agent) to make the best use of Qwen3's agentic ability. 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.

from qwen_agent.agents import Assistant

# Define LLM
llm_cfg = {
    'model': 'Qwen3-1.7B-FP8',

    # 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 Tools
tools = [
    {'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 Agent
bot = Assistant(llm=llm_cfg, function_list=tools)

# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
    pass
print(responses)

📚 Documentation

Model Overview

This repo contains the FP8 version of Qwen3 - 1.7B, which has the following features:

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

Note on FP8

For convenience and performance, we have provided a fp8 - quantized model checkpoint for Qwen3, whose name ends with -FP8. The quantization method is fine - grained fp8 quantization with a block size of 128. You can find more details in the quantization_config field in config.json.

You can use the Qwen3 - 1.7B - FP8 model with several inference frameworks, including transformers, sglang, and vllm, as the original bfloat16 model. However, please pay attention to the following known issues:

⚠️ Important Note

There are currently issues with the "fine - grained fp8" method in transformers for distributed inference. You may need to set the environment variable CUDA_LAUNCH_BLOCKING = 1 if multiple devices are used in inference.

Switching Between Thinking and Non - Thinking Mode

💡 Usage Tip

The enable_thinking switch is also available in APIs created by SGLang and vLLM. Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking - non - thinking - modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking - non - thinking - modes) users.

enable_thinking=True

By 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.

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.

⚠️ Important Note

For thinking mode, use Temperature = 0.6, TopP = 0.95, TopK = 20, and MinP = 0 (the default setting in generation_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](#best - practices) section.

enable_thinking=False

We 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.

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.

⚠️ Important Note

For non - thinking mode, we suggest using Temperature = 0.7, TopP = 0.8, TopK = 20, and MinP = 0. For more detailed guidance, please refer to the [Best Practices](#best - practices) section.

Best Practices

To achieve optimal performance, we recommend the following settings:

1. Sampling Parameters:
   • For thinking mode (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.
   • For non - thinking mode (enable_thinking=False), we suggest using Temperature = 0.7, TopP = 0.8, TopK = 20, and MinP = 0.
   • For supported frameworks, you can adjust the 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.
2. 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.
3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.
   • Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
   • Multiple - Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."
4. 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.

Citation

If you find our work helpful, feel free to give us a cite.

@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}, 
}

📄 License

This project is licensed under the Apache - 2.0 license.