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.gitattributes CHANGED
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README.md CHANGED
@@ -1,9 +1,123 @@
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- ---
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- license: apache-2.0
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- datasets:
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- - code-search-net/code_search_net
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- base_model:
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- - Qwen/Qwen3-32B
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- pipeline_tag: text-generation
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- library_name: transformers
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Qwen3-32B-AWQ-Code1080
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+
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+ ## Qwen3-AWQ Highlights
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+
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+ - Open-source. Calibration data, evaluation tools, and model quantization algorithms are fully open-source.
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+ - Precision. Achieves lower accuracy loss compared to officially quantized models.
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+ - Process. Provides detailed quantization and testing workflows for easy reproducibility.
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+ - Faster. The AutoQuant kernel has been released in [vLLM](https://github.com/Adlik/vllm/tree/vllm_0.8.5_autoquant), delivering superior performance compared to the Marlin kernel.
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+
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+ ## Model Overview
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+
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+ **Qwen3-32B** has the following features:
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+
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+ - Type: Causal Language Models
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+ - Training Stage: Pretraining & Post-training
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+ - Number of Parameters: 32.8B
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+ - Number of Paramaters (Non-Embedding): 31.2B
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+ - Number of Layers: 64
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+ - Number of Attention Heads (GQA): 64 for Q and 8 for KV
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+ - Context Length: 32,768 natively and [131,072 tokens with YaRN](https://huggingface.co/Qwen/Qwen3-32B-AWQ#processing-long-texts).
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+ - Quantization: AWQ 4-bit
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+
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+ For more details, including benchmark evaluation and inference performance, please refer to our [GitHub](https://github.com/Adlik).
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+
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+ ## Quantization
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+
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+ - calibration data
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+
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+ The model quantization process uses the Pile dataset for calibration. You can download the data from https://github.com/Adlik/model_zoo/tree/main/LLM/datasets/code_6in1_1080.jsonl.
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+
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+ - quantization algorithm
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+
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+ The model quantization process employs two quantization algorithms: AWQ and GPTQ. We have modified [AutoAWQ](https://github.com/Adlik/AutoAWQ/tree/autoawq_qwen3) and [AutoGPTQ](https://github.com/Adlik/AutoGPTQ/tree/qwen3_quant)frameworks for this purpose, which are directly usable.
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+
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+ ## Evaluation
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+
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+ For deployment, we use vllm==0.8.5 and create an OpenAI-compatible API endpoint:
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+
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+ nothink:
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+
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+ ```bash
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+ VLLM_USE_MODELSCOPE=True CUDA_VISIBLE_DEVICES=0,1 vllm serve /model --gpu-memory-utilization 0.9 --served-model-name Qwen3-32B --trust_remote_code --port 48001 --tensor-parallel-size 2
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+ ```
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+
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+ think:
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+
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+ ```bash
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+ VLLM_USE_MODELSCOPE=True CUDA_VISIBLE_DEVICES=0,1 vllm serve /model --gpu-memory-utilization 0.9 --served-model-name Qwen3-32B --trust_remote_code --port 48001 --tensor-parallel-size 2 --enable-reasoning --reasoning-parser deepseek_r1
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+ ```
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+
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+ Sampling parameters are set to match https://huggingface.co/Qwen/Qwen3-32B#best-practices.
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+
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+ To facilitate testing and reproducibility, we utilized the open-source [evalscope](https://github.com/modelscope/evalscope)tool to evaluate the accuracy of both bfloat16 (BF16) and quantized models.
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+
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+ ```shell
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+ git clone https://github.com/modelscope/evalscope.git
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+ git checkout -b v0.17.0 tags/v0.17.0
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+ cd evalscope/
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+ pip install -e .
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+ ```
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+
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+ ## Performance
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+
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+ ### Benchmarks
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+
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+ All test results were obtained on the following hardware:
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+
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+ - 4x NVIDIA A100-40G GPUs
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+ - 2x NVIDIA H800-80G GPUs
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+
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+ | model\benchmarks | think/non-think | math_500 | AIME 2024 | AIME 2025 | MMLU-REDUX | GPQA-Diamond | ceval | gsm8k | ifeval | iquiz | trivia_qa | CMMLU | mmlu |
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+ | -------------------------- | --------------- | -------- | --------- | --------- | ---------- | ------------ | ----- | ----- | ------ | ----- | --------- | ----- | ----- |
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+ | qwen3-32B-AWQ(paper) | think | \ | 79.4 | \ | 90.8 | 69.0 | \ | \ | \ | \ | \ | \ | \ |
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+ | | non-think | \ | \ | \ | 85.6 | 53.1 | \ | \ | \ | \ | \ | \ | \ |
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+ | qwen3-32B-AWQ(self-test) | think | 95.2 | 76.67 | 73.33 | 89.09 | 67.68 | 88.41 | 92.04 | 85.35 | 80.83 | 79.63 | 86.74 | 86.2 |
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+ | | non-think | 83.2 | 36.67 | 13.33 | 86.26 | 56.57 | 85.66 | 87.49 | 86.74 | 79.17 | 73.69 | 84.53 | 82.49 |
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+ | Qwen3-32B-AWQ-Code1080 | think | 94.4 | 86.67 | 73.34 | 88.18 | 71.72 | 88.34 | 93.56 | 88.21 | 81.67 | 78.62 | 86.36 | 86.43 |
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+ | | non-think | 83.6 | 26.67 | 26.66 | 85.98 | 57.07 | 84.92 | 89.39 | 87.77 | 79.17 | 72.59 | 84.54 | 82.04 |
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+
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+ ### Performance
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+
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+ - 2 x A100-40GB
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+ - vllm0.8.5
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+
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+ "To use AutoQuant, simply modify the `config.json` file as shown below:
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+
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+ ```json
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+ "quantization_config": {
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+ "bits": 4,
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+ "group_size": 128,
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+ "modules_to_not_convert": null,
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+ "quant_method": "autoquant", // change from "awq" to "autoquant"
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+ "version": "gemm",
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+ "zero_point": true
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+ },
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+ ```
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+
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+ ```shell
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+ # throughput
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+ CUDA_VISIBLE_DEVICES=4,5 python3 benchmark_throughput.py --model /model --input-len 1024 --output-len 1024 -tp 2 --max-model-len 40960 --num-prompts 100
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+
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+ # latency
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+ CUDA_VISIBLE_DEVICES=4,5 python3 benchmark_latency.py --model /model --num-iters-warmup 10 --num-iters 50 --batch-size 16 --input-len 512 --output-len 512 -tp 2
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+ ```
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+
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+ - Throughput
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+
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+ | kernel\\(tokens/s) | type | in/out=512 | in/out=1024 | in/out=2048 | in/out=4096 |
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+ | ------------------ | ------ | ---------- | ----------- | ----------- | ----------- |
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+ | awq_marlin | total | 2153.85 | 1875.67 | 1310.74 | 910.41 |
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+ | | output | 1046.28 | 910.15 | 638.11 | 438.71 |
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+ | autoquant | total | 2453.12 | 2111.43 | 1416.66 | 963.93 |
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+ | | output | 1198.05 | 1024.29 | 689.29 | 469.88 |
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+
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+ - Latency(average)
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+
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+ | kernel\second | batch | in/out=128 | in/out=512 | in/out=1024 | in/out=2048 |
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+ | ------------- | ----- | ---------- | ---------- | ----------- | ----------- |
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+ | awq_marlin | 16 | 2.4654 | 10.1091 | 21.3455 | 47.7168 |
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+ | | 64 | 4.8633 | 20.8356 | 47.3302 | 170.8086 |
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+ | autoquant | 16 | 2.3916 | 9.9021 | 21.0006 | 46.9298 |
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+ | | 64 | 4.7231 | 20.2468 | 46.0811 | 168.4375 |
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+
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+ "lstrip": false,
80
+ "normalized": false,
81
+ "rstrip": false,
82
+ "single_word": false,
83
+ "special": true
84
+ },
85
+ "151653": {
86
+ "content": "<|vision_end|>",
87
+ "lstrip": false,
88
+ "normalized": false,
89
+ "rstrip": false,
90
+ "single_word": false,
91
+ "special": true
92
+ },
93
+ "151654": {
94
+ "content": "<|vision_pad|>",
95
+ "lstrip": false,
96
+ "normalized": false,
97
+ "rstrip": false,
98
+ "single_word": false,
99
+ "special": true
100
+ },
101
+ "151655": {
102
+ "content": "<|image_pad|>",
103
+ "lstrip": false,
104
+ "normalized": false,
105
+ "rstrip": false,
106
+ "single_word": false,
107
+ "special": true
108
+ },
109
+ "151656": {
110
+ "content": "<|video_pad|>",
111
+ "lstrip": false,
112
+ "normalized": false,
113
+ "rstrip": false,
114
+ "single_word": false,
115
+ "special": true
116
+ },
117
+ "151657": {
118
+ "content": "<tool_call>",
119
+ "lstrip": false,
120
+ "normalized": false,
121
+ "rstrip": false,
122
+ "single_word": false,
123
+ "special": false
124
+ },
125
+ "151658": {
126
+ "content": "</tool_call>",
127
+ "lstrip": false,
128
+ "normalized": false,
129
+ "rstrip": false,
130
+ "single_word": false,
131
+ "special": false
132
+ },
133
+ "151659": {
134
+ "content": "<|fim_prefix|>",
135
+ "lstrip": false,
136
+ "normalized": false,
137
+ "rstrip": false,
138
+ "single_word": false,
139
+ "special": false
140
+ },
141
+ "151660": {
142
+ "content": "<|fim_middle|>",
143
+ "lstrip": false,
144
+ "normalized": false,
145
+ "rstrip": false,
146
+ "single_word": false,
147
+ "special": false
148
+ },
149
+ "151661": {
150
+ "content": "<|fim_suffix|>",
151
+ "lstrip": false,
152
+ "normalized": false,
153
+ "rstrip": false,
154
+ "single_word": false,
155
+ "special": false
156
+ },
157
+ "151662": {
158
+ "content": "<|fim_pad|>",
159
+ "lstrip": false,
160
+ "normalized": false,
161
+ "rstrip": false,
162
+ "single_word": false,
163
+ "special": false
164
+ },
165
+ "151663": {
166
+ "content": "<|repo_name|>",
167
+ "lstrip": false,
168
+ "normalized": false,
169
+ "rstrip": false,
170
+ "single_word": false,
171
+ "special": false
172
+ },
173
+ "151664": {
174
+ "content": "<|file_sep|>",
175
+ "lstrip": false,
176
+ "normalized": false,
177
+ "rstrip": false,
178
+ "single_word": false,
179
+ "special": false
180
+ },
181
+ "151665": {
182
+ "content": "<tool_response>",
183
+ "lstrip": false,
184
+ "normalized": false,
185
+ "rstrip": false,
186
+ "single_word": false,
187
+ "special": false
188
+ },
189
+ "151666": {
190
+ "content": "</tool_response>",
191
+ "lstrip": false,
192
+ "normalized": false,
193
+ "rstrip": false,
194
+ "single_word": false,
195
+ "special": false
196
+ },
197
+ "151667": {
198
+ "content": "<think>",
199
+ "lstrip": false,
200
+ "normalized": false,
201
+ "rstrip": false,
202
+ "single_word": false,
203
+ "special": false
204
+ },
205
+ "151668": {
206
+ "content": "</think>",
207
+ "lstrip": false,
208
+ "normalized": false,
209
+ "rstrip": false,
210
+ "single_word": false,
211
+ "special": false
212
+ }
213
+ },
214
+ "additional_special_tokens": [
215
+ "<|im_start|>",
216
+ "<|im_end|>",
217
+ "<|object_ref_start|>",
218
+ "<|object_ref_end|>",
219
+ "<|box_start|>",
220
+ "<|box_end|>",
221
+ "<|quad_start|>",
222
+ "<|quad_end|>",
223
+ "<|vision_start|>",
224
+ "<|vision_end|>",
225
+ "<|vision_pad|>",
226
+ "<|image_pad|>",
227
+ "<|video_pad|>"
228
+ ],
229
+ "bos_token": null,
230
+ "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>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].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 {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
231
+ "clean_up_tokenization_spaces": false,
232
+ "eos_token": "<|im_end|>",
233
+ "errors": "replace",
234
+ "model_max_length": 131072,
235
+ "pad_token": "<|endoftext|>",
236
+ "split_special_tokens": false,
237
+ "tokenizer_class": "Qwen2Tokenizer",
238
+ "unk_token": null
239
+ }
vocab.json ADDED
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