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Model: XGenerationLab/XiYanSQL-QwenCoder-14B-2502 Source: Original Platform
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README.md
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---
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frameworks:
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- Pytorch
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license: Apache License 2.0
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tasks:
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- text-generation
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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tags:
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- NL2SQL
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- SQL
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- Text-to-SQL
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---
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### Important Links
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🤖[Github](https://github.com/XGenerationLab/XiYanSQL-QwenCoder) |
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💻[HuggingFace](https://huggingface.co/collections/XGenerationLab/xiyansql-models-67c9844307b49f87436808fc) |
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📖[XiYan-SQL](https://github.com/XGenerationLab/XiYan-SQL) |
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🌕[析言GBI](https://bailian.console.aliyun.com/xiyan) |
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🤗[Modelscope Space](https://www.modelscope.cn/studios/XGenerationLab/XiYanSQL-QwenCoder-32B)
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## Introduction
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We are excited to open source the XiYanSQL-QwenCoder series model, dedicated to advancing the development of LLMs in the text-to-SQL domain. As of now, XiYanSQL-QwenCoder covers four mainstream model sizes: 3B, 7B, 14B, and 32B parameters, to meet the needs of different developers.
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- The XiYanSQL-QwenCoder model demonstrates strong performance in SQL generation, with the XiYanSQL-QwenCoder-32B achieving a 69.03% EX score on the BIRD TEST set, setting a new SOTA with a single fine-tuned model. Other models in the series also maintain a leading position at their respective sizes.
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- The XiYanSQL-QwenCoder model supports multiple SQL dialects, such as SQLite, PostgreSQL, and MySQL.
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- The XiYanSQL-QwenCoder model can be used directly for text-to-SQL tasks or serve as a better starting point for fine-tuning SQL models.
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## Model Downloads
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| **Model** | **Download Latest** |
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|-----------|------------------|
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|XiYanSQL-QwenCoder-3B |💻[HuggingFace](https://huggingface.co/XGenerationLab/XiYanSQL-QwenCoder-3B-2502) 🤗[Modelscope](https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-3B-2502)|
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|XiYanSQL-QwenCoder-7B |💻[HuggingFace](https://huggingface.co/XGenerationLab/XiYanSQL-QwenCoder-7B-2502) 🤗[Modelscope](https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-7B-2502)|
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|XiYanSQL-QwenCoder-14B |💻[HuggingFace](https://huggingface.co/XGenerationLab/XiYanSQL-QwenCoder-14B-2502) 🤗[Modelscope](https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-14B-2502)|
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|XiYanSQL-QwenCoder-32B |💻[HuggingFace](https://huggingface.co/XGenerationLab/XiYanSQL-QwenCoder-32B-2412) 🤗[Modelscope](https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-32B-2412)|
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## Performance
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The XiYanSQL-QwenCoder models, as multi-dialect SQL base models, demonstrating robust SQL generation capabilities. The following presents the evaluation results at the time of release. We conducted a comprehensive evaluation of the model's performance under two schema formats, M-Schema, and original DDL, using the BIRD and Spider benchmarks in the Text-to-SQL domain.
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| Model name|BIRD Dev@M-Schema |BIRD Dev@DDL|Spider Test@M-Schema|Spider Test@DDL|
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|-----------|:------------------:|:---------------:|:-------------------:|:---------------:|
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|Codellama-34b | 33.05% | - | 67.74% | - |
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|Deepseek-coder-33b | 47.52% | 44.72% | 72.39% | - |
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|TableGPT2 | 46.35% | 47.07% | 74.76% | 77.28% |
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|Codestral 22b | 50.52% | 47.00% | 78.45% | 75.47% |
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|GLM-4-plus | 54.37% | - | 79.40% | - |
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|Claude35_sonnet-1022 | 53.32% | 50.46% | 76.27% | 73.04% |
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|Deepseek(v2.5-1210) | 55.74% | 55.61% | 82.08% | 80.57% |
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|Gemini-1.5-pro | 61.34% | 57.89% | 85.11% | 84.00% |
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|GPT-4o-0806 | 58.47% | 54.82% | 82.89% | 78.45% |
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|XiYanSQL-QwenCoder-3B | 54.11% | 53.19% | 82.69% | 78.85% |
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|XiYanSQL-QwenCoder-7B | 59.78% | 56.58% | 84.86% | 80.31% |
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|XiYanSQL-QwenCoder-14B | 63.10% | 60.37% | 85.76% | 82.79% |
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|XiYanSQL-QwenCoder-32B | 67.01% | 63.04% | 88.39% | 85.46% |
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## Requirements
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transformers >= 4.37.0
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## Quickstart
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Here is a simple code snippet for quickly using **XiYanSQL-QwenCoder** model. We provide a Chinese version of the prompt, and you just need to replace the placeholders for "question," "db_schema," and "evidence" to get started. We recommend using our [M-Schema](https://github.com/XGenerationLab/M-Schema) format for the schema; other formats such as DDL are also acceptable, but they may affect performance.
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Currently, we mainly support mainstream dialects like SQLite, PostgreSQL, and MySQL.
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```
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nl2sqlite_template_cn = """你是一名{dialect}专家，现在需要阅读并理解下面的【数据库schema】描述，以及可能用到的【参考信息】，并运用{dialect}知识生成sql语句回答【用户问题】。
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【用户问题】
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{question}
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【数据库schema】
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{db_schema}
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【参考信息】
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{evidence}
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【用户问题】
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{question}
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```sql"""
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "XGenerationLab/XiYanSQL-QwenCoder-14B-2502"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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## dialects -> ['SQLite', 'PostgreSQL', 'MySQL']
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prompt = nl2sqlite_template_cn.format(dialect="", db_schema="", question="", evidence="")
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message = [{'role': 'user', 'content': prompt}]
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text = tokenizer.apply_chat_template(
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message,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id,
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max_new_tokens=1024,
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temperature=0.1,
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top_p=0.8,
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do_sample=True,
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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### Inference with vLLM
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```python
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from vllm import LLM, SamplingParams
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from transformers import AutoTokenizer
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model_path = "XGenerationLab/XiYanSQL-QwenCoder-14B-2502"
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llm = LLM(model=model_path, tensor_parallel_size=8)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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sampling_params = SamplingParams(
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n=1,
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temperature=0.1,
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max_tokens=1024
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)
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## dialects -> ['SQLite', 'PostgreSQL', 'MySQL']
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prompt = nl2sqlite_template_cn.format(dialect="", db_schema="", question="", evidence="")
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message = [{'role': 'user', 'content': prompt}]
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text = tokenizer.apply_chat_template(
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message,
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tokenize=False,
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add_generation_prompt=True
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)
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outputs = llm.generate([text], sampling_params=sampling_params)
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response = outputs[0].outputs[0].text
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```
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## Acknowledgments
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If you find our work useful, please give us a citation or a like, so we can make a greater contribution to the open-source community!
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```bibtex
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@article{XiYanSQL,
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title={XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL},
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author={Yifu Liu and Yin Zhu and Yingqi Gao and Zhiling Luo and Xiaoxia Li and Xiaorong Shi and Yuntao Hong and Jinyang Gao and Yu Li and Bolin Ding and Jingren Zhou},
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year={2025},
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eprint={2507.04701},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2507.04701},
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}
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```
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config.json
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"_name_or_path": "/mnt/model_cache/Qwen2___5-Coder-14B-Instruct/",
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"architectures": [
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"Qwen2ForCausalLM"
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"max_window_layers": 48,
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"model_type": "qwen2",
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"num_attention_heads": 40,
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"num_hidden_layers": 48,
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"num_key_value_heads": 8,
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"tie_word_embeddings": false,
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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{"framework":"Pytorch","task":"text-generation"}
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31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
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||||
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||||
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||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
303283
tokenizer.json
Normal file
303283
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
207
tokenizer_config.json
Normal file
207
tokenizer_config.json
Normal file
@@ -0,0 +1,207 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\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 {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.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 %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 8192,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
45624
trainer_state.json
Normal file
45624
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:2244e6e243b6e2812b75d332d4c530de318ee3de7f7cc7b6a691bf9eeb916ab6
|
||||
size 9656
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user