Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +123 -9
- added_tokens.json +28 -0
- chat_template.jinja +85 -0
- config.json +38 -0
- generation_config.json +13 -0
- merges.txt +0 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +0 -0
- quant_config.json +8 -0
- special_tokens_map.json +31 -0
- tokenizer.json +3 -0
- tokenizer_config.json +239 -0
- vocab.json +0 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
CHANGED
@@ -1,9 +1,123 @@
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# Qwen3-32B-AWQ-Code1080
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## Qwen3-AWQ Highlights
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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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## Model Overview
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**Qwen3-32B** has the following features:
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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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For more details, including benchmark evaluation and inference performance, please refer to our [GitHub](https://github.com/Adlik).
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## Quantization
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- calibration data
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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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- quantization algorithm
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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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## Evaluation
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For deployment, we use vllm==0.8.5 and create an OpenAI-compatible API endpoint:
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nothink:
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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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think:
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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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Sampling parameters are set to match https://huggingface.co/Qwen/Qwen3-32B#best-practices.
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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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```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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## Performance
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### Benchmarks
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All test results were obtained on the following hardware:
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- 4x NVIDIA A100-40G GPUs
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- 2x NVIDIA H800-80G GPUs
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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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### Performance
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- 2 x A100-40GB
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- vllm0.8.5
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"To use AutoQuant, simply modify the `config.json` file as shown below:
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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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```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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# 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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- Throughput
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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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- Latency(average)
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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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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
|
4 |
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# 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>" }}
|
7 |
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{%- for tool in tools %}
|
8 |
+
{{- "\n" }}
|
9 |
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{{- tool | tojson }}
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{%- endfor %}
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11 |
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{{- "\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" }}
|
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+
{%- else %}
|
13 |
+
{%- if messages[0].role == 'system' %}
|
14 |
+
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
15 |
+
{%- endif %}
|
16 |
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{%- endif %}
|
17 |
+
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
18 |
+
{%- for message in messages[::-1] %}
|
19 |
+
{%- set index = (messages|length - 1) - loop.index0 %}
|
20 |
+
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
21 |
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{%- set ns.multi_step_tool = false %}
|
22 |
+
{%- set ns.last_query_index = index %}
|
23 |
+
{%- endif %}
|
24 |
+
{%- endfor %}
|
25 |
+
{%- for message in messages %}
|
26 |
+
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
27 |
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
28 |
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{%- elif message.role == "assistant" %}
|
29 |
+
{%- set content = message.content %}
|
30 |
+
{%- set reasoning_content = '' %}
|
31 |
+
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
|
32 |
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{%- set reasoning_content = message.reasoning_content %}
|
33 |
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{%- else %}
|
34 |
+
{%- if '</think>' in message.content %}
|
35 |
+
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
|
36 |
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{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
37 |
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{%- endif %}
|
38 |
+
{%- endif %}
|
39 |
+
{%- if loop.index0 > ns.last_query_index %}
|
40 |
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{%- if loop.last or (not loop.last and reasoning_content) %}
|
41 |
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
42 |
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{%- else %}
|
43 |
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{{- '<|im_start|>' + message.role + '\n' + content }}
|
44 |
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{%- endif %}
|
45 |
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{%- else %}
|
46 |
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{{- '<|im_start|>' + message.role + '\n' + content }}
|
47 |
+
{%- endif %}
|
48 |
+
{%- if message.tool_calls %}
|
49 |
+
{%- for tool_call in message.tool_calls %}
|
50 |
+
{%- if (loop.first and content) or (not loop.first) %}
|
51 |
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{{- '\n' }}
|
52 |
+
{%- endif %}
|
53 |
+
{%- if tool_call.function %}
|
54 |
+
{%- set tool_call = tool_call.function %}
|
55 |
+
{%- endif %}
|
56 |
+
{{- '<tool_call>\n{"name": "' }}
|
57 |
+
{{- tool_call.name }}
|
58 |
+
{{- '", "arguments": ' }}
|
59 |
+
{%- if tool_call.arguments is string %}
|
60 |
+
{{- tool_call.arguments }}
|
61 |
+
{%- else %}
|
62 |
+
{{- tool_call.arguments | tojson }}
|
63 |
+
{%- endif %}
|
64 |
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{{- '}\n</tool_call>' }}
|
65 |
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{%- endfor %}
|
66 |
+
{%- endif %}
|
67 |
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{{- '<|im_end|>\n' }}
|
68 |
+
{%- elif message.role == "tool" %}
|
69 |
+
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
70 |
+
{{- '<|im_start|>user' }}
|
71 |
+
{%- endif %}
|
72 |
+
{{- '\n<tool_response>\n' }}
|
73 |
+
{{- message.content }}
|
74 |
+
{{- '\n</tool_response>' }}
|
75 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
76 |
+
{{- '<|im_end|>\n' }}
|
77 |
+
{%- endif %}
|
78 |
+
{%- endif %}
|
79 |
+
{%- endfor %}
|
80 |
+
{%- if add_generation_prompt %}
|
81 |
+
{{- '<|im_start|>assistant\n' }}
|
82 |
+
{%- if enable_thinking is defined and enable_thinking is false %}
|
83 |
+
{{- '<think>\n\n</think>\n\n' }}
|
84 |
+
{%- endif %}
|
85 |
+
{%- endif %}
|
config.json
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generation_config.json
ADDED
@@ -0,0 +1,13 @@
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merges.txt
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model-00001-of-00002.safetensors
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quant_config.json
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special_tokens_map.json
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@@ -0,0 +1,31 @@
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tokenizer_config.json
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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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|
|