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README.md ADDED
@@ -0,0 +1,236 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ tags:
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+ - unsloth
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+ base_model:
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+ - Qwen/Qwen3-4B-Instruct-2507
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+ library_name: transformers
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+ license: apache-2.0
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+ license_link: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507/blob/main/LICENSE
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+ pipeline_tag: text-generation
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+ ---
11
+ > [!NOTE]
12
+ > Includes Unsloth **chat template fixes**! <br> For `llama.cpp`, use `--jinja`
13
+ >
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+
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+ <div>
16
+ <p style="margin-top: 0;margin-bottom: 0;">
17
+ <em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
18
+ </p>
19
+ <div style="display: flex; gap: 5px; align-items: center; ">
20
+ <a href="https://github.com/unslothai/unsloth/">
21
+ <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
22
+ </a>
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+ <a href="https://discord.gg/unsloth">
24
+ <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
25
+ </a>
26
+ <a href="https://docs.unsloth.ai/">
27
+ <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
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+ </a>
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+ </div>
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+ </div>
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+
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+
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+ # Qwen3-4B-Instruct-2507
34
+ <a href="https://chat.qwen.ai" target="_blank" style="margin: 2px;">
35
+ <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
36
+ </a>
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+
38
+ ## Highlights
39
+
40
+ We introduce the updated version of the **Qwen3-4B non-thinking mode**, named **Qwen3-4B-Instruct-2507**, featuring the following key enhancements:
41
+
42
+ - **Significant improvements** in general capabilities, including **instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage**.
43
+ - **Substantial gains** in long-tail knowledge coverage across **multiple languages**.
44
+ - **Markedly better alignment** with user preferences in **subjective and open-ended tasks**, enabling more helpful responses and higher-quality text generation.
45
+ - **Enhanced capabilities** in **256K long-context understanding**.
46
+
47
+ ![image/jpeg](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3-2507/Qwen3-4B-Instruct.001.jpeg)
48
+
49
+ ## Model Overview
50
+
51
+ **Qwen3-4B-Instruct-2507** has the following features:
52
+ - Type: Causal Language Models
53
+ - Training Stage: Pretraining & Post-training
54
+ - Number of Parameters: 4.0B
55
+ - Number of Paramaters (Non-Embedding): 3.6B
56
+ - Number of Layers: 36
57
+ - Number of Attention Heads (GQA): 32 for Q and 8 for KV
58
+ - Context Length: **262,144 natively**.
59
+
60
+ **NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**
61
+
62
+ 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/).
63
+
64
+
65
+ ## Performance
66
+
67
+ | | GPT-4.1-nano-2025-04-14 | Qwen3-30B-A3B Non-Thinking | Qwen3-4B Non-Thinking | Qwen3-4B-Instruct-2507 |
68
+ |--- | --- | --- | --- | --- |
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+ | **Knowledge** | | | |
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+ | MMLU-Pro | 62.8 | 69.1 | 58.0 | **69.6** |
71
+ | MMLU-Redux | 80.2 | 84.1 | 77.3 | **84.2** |
72
+ | GPQA | 50.3 | 54.8 | 41.7 | **62.0** |
73
+ | SuperGPQA | 32.2 | 42.2 | 32.0 | **42.8** |
74
+ | **Reasoning** | | | |
75
+ | AIME25 | 22.7 | 21.6 | 19.1 | **47.4** |
76
+ | HMMT25 | 9.7 | 12.0 | 12.1 | **31.0** |
77
+ | ZebraLogic | 14.8 | 33.2 | 35.2 | **80.2** |
78
+ | LiveBench 20241125 | 41.5 | 59.4 | 48.4 | **63.0** |
79
+ | **Coding** | | | |
80
+ | LiveCodeBench v6 (25.02-25.05) | 31.5 | 29.0 | 26.4 | **35.1** |
81
+ | MultiPL-E | 76.3 | 74.6 | 66.6 | **76.8** |
82
+ | Aider-Polyglot | 9.8 | **24.4** | 13.8 | 12.9 |
83
+ | **Alignment** | | | |
84
+ | IFEval | 74.5 | **83.7** | 81.2 | 83.4 |
85
+ | Arena-Hard v2* | 15.9 | 24.8 | 9.5 | **43.4** |
86
+ | Creative Writing v3 | 72.7 | 68.1 | 53.6 | **83.5** |
87
+ | WritingBench | 66.9 | 72.2 | 68.5 | **83.4** |
88
+ | **Agent** | | | |
89
+ | BFCL-v3 | 53.0 | 58.6 | 57.6 | **61.9** |
90
+ | TAU1-Retail | 23.5 | 38.3 | 24.3 | **48.7** |
91
+ | TAU1-Airline | 14.0 | 18.0 | 16.0 | **32.0** |
92
+ | TAU2-Retail | - | 31.6 | 28.1 | **40.4** |
93
+ | TAU2-Airline | - | 18.0 | 12.0 | **24.0** |
94
+ | TAU2-Telecom | - | **18.4** | 17.5 | 13.2 |
95
+ | **Multilingualism** | | | |
96
+ | MultiIF | 60.7 | **70.8** | 61.3 | 69.0 |
97
+ | MMLU-ProX | 56.2 | **65.1** | 49.6 | 61.6 |
98
+ | INCLUDE | 58.6 | **67.8** | 53.8 | 60.1 |
99
+ | PolyMATH | 15.6 | 23.3 | 16.6 | **31.1** |
100
+
101
+ *: For reproducibility, we report the win rates evaluated by GPT-4.1.
102
+
103
+
104
+ ## Quickstart
105
+
106
+ The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
107
+
108
+ With `transformers<4.51.0`, you will encounter the following error:
109
+ ```
110
+ KeyError: 'qwen3'
111
+ ```
112
+
113
+ The following contains a code snippet illustrating how to use the model generate content based on given inputs.
114
+ ```python
115
+ from transformers import AutoModelForCausalLM, AutoTokenizer
116
+
117
+ model_name = "Qwen/Qwen3-4B-Instruct-2507"
118
+
119
+ # load the tokenizer and the model
120
+ tokenizer = AutoTokenizer.from_pretrained(model_name)
121
+ model = AutoModelForCausalLM.from_pretrained(
122
+ model_name,
123
+ torch_dtype="auto",
124
+ device_map="auto"
125
+ )
126
+
127
+ # prepare the model input
128
+ prompt = "Give me a short introduction to large language model."
129
+ messages = [
130
+ {"role": "user", "content": prompt}
131
+ ]
132
+ text = tokenizer.apply_chat_template(
133
+ messages,
134
+ tokenize=False,
135
+ add_generation_prompt=True,
136
+ )
137
+ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
138
+
139
+ # conduct text completion
140
+ generated_ids = model.generate(
141
+ **model_inputs,
142
+ max_new_tokens=16384
143
+ )
144
+ output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
145
+
146
+ content = tokenizer.decode(output_ids, skip_special_tokens=True)
147
+
148
+ print("content:", content)
149
+ ```
150
+
151
+ For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
152
+ - SGLang:
153
+ ```shell
154
+ python -m sglang.launch_server --model-path Qwen/Qwen3-4B-Instruct-2507 --context-length 262144
155
+ ```
156
+ - vLLM:
157
+ ```shell
158
+ vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144
159
+ ```
160
+
161
+ **Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.**
162
+
163
+ For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
164
+
165
+ ## Agentic Use
166
+
167
+ 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.
168
+
169
+ 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.
170
+ ```python
171
+ from qwen_agent.agents import Assistant
172
+
173
+ # Define LLM
174
+ llm_cfg = {
175
+ 'model': 'Qwen3-4B-Instruct-2507',
176
+
177
+ # Use a custom endpoint compatible with OpenAI API:
178
+ 'model_server': 'http://localhost:8000/v1', # api_base
179
+ 'api_key': 'EMPTY',
180
+ }
181
+
182
+ # Define Tools
183
+ tools = [
184
+ {'mcpServers': { # You can specify the MCP configuration file
185
+ 'time': {
186
+ 'command': 'uvx',
187
+ 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
188
+ },
189
+ "fetch": {
190
+ "command": "uvx",
191
+ "args": ["mcp-server-fetch"]
192
+ }
193
+ }
194
+ },
195
+ 'code_interpreter', # Built-in tools
196
+ ]
197
+
198
+ # Define Agent
199
+ bot = Assistant(llm=llm_cfg, function_list=tools)
200
+
201
+ # Streaming generation
202
+ messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
203
+ for responses in bot.run(messages=messages):
204
+ pass
205
+ print(responses)
206
+ ```
207
+
208
+ ## Best Practices
209
+
210
+ To achieve optimal performance, we recommend the following settings:
211
+
212
+ 1. **Sampling Parameters**:
213
+ - We suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
214
+ - 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.
215
+
216
+ 2. **Adequate Output Length**: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models.
217
+
218
+ 3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
219
+ - **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
220
+ - **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"`."
221
+
222
+ ### Citation
223
+
224
+ If you find our work helpful, feel free to give us a cite.
225
+
226
+ ```
227
+ @misc{qwen3technicalreport,
228
+ title={Qwen3 Technical Report},
229
+ author={Qwen Team},
230
+ year={2025},
231
+ eprint={2505.09388},
232
+ archivePrefix={arXiv},
233
+ primaryClass={cs.CL},
234
+ url={https://arxiv.org/abs/2505.09388},
235
+ }
236
+ ```
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+ {{- '<|im_start|>system\n' }}
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+ {%- endif %}
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+ {{- tool | tojson }}
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+ "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = ((content.split('</think>')|first).rstrip('\\n').split('<think>')|last).lstrip('\\n') %}\n {%- set content = (content.split('</think>')|last).lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}"
241
+ }
vocab.json ADDED
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