--- title: Qwen3-8B-Int4-W4A16 canonical_url: "https://www.modelscope.cn/models/okwinds/Qwen3-8B-Int4-W4A16" md_url: "https://www.modelscope.cn/models/okwinds/Qwen3-8B-Int4-W4A16.md" repository: okwinds/Qwen3-8B-Int4-W4A16 chinese_name: "通义千问 Qwen3-8B-Int4-W4A16 量化高精校准" last_updated: 2025-05-29 license: apache-2.0 pipeline_tag: text-generation tasks: - text-generation model_type: - qwen3 architectures: - Qwen3ForCausalLM base_model: - Qwen/Qwen3-8B base_model_relation: quantized parameters: 2.2B tensor_type: - BF16 - I64 - I32 library_name: - transformer - safetensors - pytorch frameworks: - Pytorch language: - en - zh inference_backends: - "deploy_task text/emb" - "lmdeploy_turbomind 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 702 stars: 2 tags: - Instruct - Chat - "Reason Model" - Quantization --- # Qwen3-8B-Int4-W4A16 > Qwen3-8B-Int4-W4A16 - okwinds 在 ModelScope 开源的模型。重要：友情提醒，推理本模型时，请按照如下官方指引： okwinds/Qwen3-8B-Int4-W4A16 是 ModelScope 魔搭社区上的 2.2B 参数text-generation模型，采用 apache-2.0 许可，基于 Qwen/Qwen3-8B 构建，可用 deploy_task text/emb、lmdeploy_turbomind 0.9.1、sglang 0.5.2 部署。 - **Repository**: okwinds/Qwen3-8B-Int4-W4A16 - **License**: apache-2.0 - **Tasks**: text-generation - **Parameters**: 2.2B - **Base model**: Qwen/Qwen3-8B - **Inference backends**: deploy_task text/emb, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Tags**: Instruct, Chat, Reason Model, Quantization - **Downloads**: 702 - **Stars**: 2 - **Last updated**: 2025-05-29 Source: https://www.modelscope.cn/models/okwinds/Qwen3-8B-Int4-W4A16 --- ### 重要：友情提醒，推理本模型时，请按照如下官方指引： #### 对于思考模式（enable_thinking=True， 默认为 True） > 请使用 Temperature=0.6 、 TopP=0.95 、 TopK=20 和 MinP=0 （ generation_config.json 中的默认设置）。请勿使用贪婪解码（greedy decoding），因为它可能导致性能下降和无限循环。如需更详细的指导，请参阅最佳实践部分。 #### 对于非思考模式（enable_thinking=False） > 建议使用 Temperature=0.7 、 TopP=0.8 、 TopK=20 和 MinP=0 。如需更详细的指导，请参阅最佳实践部分。 #### 📖 关于量化损失方面的研究，可阅读公众号“觉察流”文章👇
《[Reason Model 的“瘦身计划”：量化技术的得与失](https://mp.weixin.qq.com/s/NMGq4UUkfo8GMix5LHnWCg)》 # 通义千问 Qwen3-8B-Int4-W4A16 量化高精校准 原模型 [qwen/Qwen3-8B](https://www.modelscope.cn/models/Qwen/Qwen3-8B) --- #### _作者在此 👇🏻 扫一扫_ --- ## 下载 SDK下载 ```bash #安装ModelScope pip install modelscope ``` ```python #SDK模型下载 from modelscope import snapshot_download model_dir = snapshot_download('okwinds/Qwen3-8B-Int4-W4A16') ``` Git下载 ``` #Git模型下载 git clone https://www.modelscope.cn/okwinds/Qwen3-8B-Int4-W4A16.git ``` ## 模型概述 Qwen3-8B-Int4-W4A16 是一个基于 通义千问 Qwen3-8B 的 INT4 量化并校准的模型。 - **模型名称:** Qwen3-8B-Int4-W4A16 - **模型架构:** Qwen3 - **权重量化:** INT4 该模型通过将 [qwen/Qwen3-8B](https://www.modelscope.cn/models/Qwen/Qwen3-8B) 的权重量化为 INT4 数据类型而实现。 量化过程将每个参数从 16bit 减少到 4bit，将模型占用磁盘空间大小，以及推理时加载模型需要的GPU显存空间，减少到了大约为原模型的1/4。只有 transformer 中的 Linear 层权重是量化的，其他层均保持为原模型数据类型，采用了混合精度的计算方式，尽可能减少模型量化后所带来的精度损失。 量化本模型，使用了[AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ)，采用 Symmetric group-wise 方式量化（group size : 128）。并在量化过程中，进行了数据校准，以提升模型生成精度。 > 注意：本模型需要 compute capability > 8.0（Ampere、Ada Lovelace、Hopper 架构）的 Nvidia GPU 来支持 INT4 **混合精度**计算。 ## 部署推理 #### 推荐使用 vLLM>=0.8.4 ( transformers>=4.51.0 ) Openai api 兼容模式 ```bash >>> vllm serve "/home/gavin/llm/Qwen3-8B-Int4-W4A16" --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.9 --served-model-name "Qwen3-8B-Int4-W4A16" ``` --- # 附录 Qwen3-8B 介绍 Chat ## Qwen3 Highlights 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: - **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios. - **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning. - **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience. - **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks. - **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**. ## Model Overview **Qwen3-8B** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 8.2B - Number of Paramaters (Non-Embedding): 6.95B - Number of Layers: 36 - Number of Attention Heads (GQA): 32 for Q and 8 for KV - Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts). For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Quickstart 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: ``` 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/Qwen3-8B" # 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 () 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>=0.8.4` or to create an OpenAI-compatible API endpoint: - SGLang: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3 ``` - vLLM: ```shell vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1 ``` For local use, applications such as llama.cpp, Ollama, LMStudio, and MLX-LM have also supported Qwen3. ## Switching Between Thinking and Non-Thinking Mode > [!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. ```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 `...` block, followed by the final response. > [!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. ```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 `...` block. > [!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. ### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input 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/Qwen3-8B"): 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}") ``` > [!NOTE] > For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `...`. However, the content inside this block may be empty if thinking is disabled. > When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `...` block. ## 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 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 LLM llm_cfg = { 'model': 'Qwen3-8B', # 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 `this is the thoughtthis 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) ``` ## Processing Long Texts Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method. YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks: - Modifying the model files: In the `config.json` file, add the `rope_scaling` fields: ```json { ..., "rope_scaling": { "type": "yarn", "factor": 4.0, "original_max_position_embeddings": 32768 } } ``` For `llama.cpp`, you need to regenerate the GGUF file after the modification. - Passing command line arguments: For `vllm`, you can use ```shell vllm serve ... --rope-scaling '{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072 ``` For `sglang`, you can use ```shell python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}' ``` For `llama-server` from `llama.cpp`, you can use ```shell llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768 ``` > [!IMPORTANT] > If you encounter the following warning > ``` > Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'} > ``` > please upgrade `transformers>=4.51.0`. > [!NOTE] > All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.** > We advise adding the `rope_scaling` configuration only when processing long contexts is required. > It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0. > [!NOTE] > The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance. > [!TIP] > The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed. ## 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{qwen3, title = {Qwen3}, url = {https://qwenlm.github.io/blog/qwen3/}, author = {Qwen Team}, month = {April}, year = {2025} } ```