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SUS-Chat-34B-3.0bpw-h6-exl2

Text Generation
Transformers
PyTorch
llama
text-generation-inference
Inference Endpoints
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  1. README.md +168 -0
  2. config.json +28 -0
  3. output-00001-of-00002.safetensors +3 -0
  4. output-00002-of-00002.safetensors +3 -0
  5. pytorch_model.bin.index.json +550 -0
  6. special_tokens_map.json +6 -0
  7. tokenizer.json +0 -0
  8. tokenizer.model +3 -0
  9. tokenizer_config.json +38 -0
README.md ADDED
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+ ---
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+ license: other
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+ license_name: yi-license
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+ license_link: LICENSE
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+ widget:
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+ - example_title: SUS-Chat
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+ text: hi
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+ output:
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+ text: ' Hello! How can I assist you today?'
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+
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+ pipeline_tag: text-generation
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+ ---
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+
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+ # 🐗SUS-Chat: Instruction tuning done right
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+
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+ <div align="center">
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+
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+ <p align="center">
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+ <img width="200px" src="https://github.com/SUSTech-IDEA/SUS-Chat/raw/main/assets/sustech.svg?sanitize=true">
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+ </p>
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+
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+ <div style="display: inline-block;">
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+
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+ <a rel="noopener nofollow" href="https://github.com/SUSTech-IDEA/SUS-Chat/issues">
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+ <img src="https://img.shields.io/github/issues/SUSTech-IDEA/SUS-Chat?logo=github" style="margin: 0 0;">
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+ </a>
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+
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+ </div>
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+
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+ <div style="display: inline-block;">
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+
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+ <a href="https://huggingface.co/SUSTech">
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+ <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-SUSTech-blue" style="margin: 0 0;">
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+ </a>
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+
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+ </div>
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+
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+ <div style="display: inline-block;">
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+
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+ <a rel="noopener nofollow" href="https://www.modelscope.cn/organization/sustc/">
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+ <img src="https://img.shields.io/badge/ModelScope-sustec-blue" style="margin: 0 0;">
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+ </a>
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+
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+ </div>
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+
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+ <div style="display: inline-block;">
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+
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+ <a rel="noopener nofollow" href="https://github.com/SUSTech-IDEA/SUS-Chat/blob/main/LICENSE">
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+ <img src="https://img.shields.io/badge/Code_License-Apache_2.0-lightblue" style="margin: 0 0;">
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+ </a>
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+
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+ </div>
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+
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+ <div style="display: inline-block;">
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+
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+ <a rel="noopener nofollow" href="https://github.com/SUSTech-IDEA/SUS-Chat/blob/main/MODEL_LICENSE_AGREEMENT.txt">
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+ <img src="https://img.shields.io/badge/Model_License-Model_Agreement-lightblue" style="margin: 0 0;">
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+ </a>
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+
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+ </div>
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+
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+ <div style="display: inline-block;">
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+
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+ <a rel="noopener nofollow" href="mailto:oss@data.sustech.edu.cn">
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+ <img src="https://img.shields.io/badge/✉️-data@sustech.edu.cn-FFE01B" style="margin: 0 0;">
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+ </a>
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+
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+ </div>
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+
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+ </div>
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+
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+ # Inrtoduction
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+
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+ <img src="https://hackmd.io/_uploads/S1dXCTIHp.png" id="fig-sus"
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+ alt="Figure 1: DALL·E 2023-12-01 11.03.28 - An imposing, majestic wild boar combined with elements of a futuristic transformer robot. The boar itself should be intricately blended with these tra" />
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+
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+ **SUS-Chat**
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+ 是一个34B的中英文对话模型，由南方科技大学和粤港澳大湾区数字经济研究院联合发布。SUS-Chat-34B模型在数百万高质、多语言的指令数据上进行了微调，在保持基础模型强大的语言能力的同时，SUS-Chat-34B模型通过高质量指令微调改善了模型对人类指令的响应方式并擅长通过思维链的方式模仿人类思考过程。
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+
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+ 它在几乎所有基准测试中超过了所有同尺寸的模型，而且能够更好地满足了复杂多语言任务的实际需求，相比于更大的模型，SUS-Chat-34B仍具有相当竞争力，在我们的综合评测中取得了最先进的表现。
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+
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+ SUS-Chat有力地证明了通过正确的指令微调，学术机构可以在不增加模型参数的情况下，通过开源的数据集和模型，获得更好的性能,
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+ 这弥合了学术界和工业界的在大语言模型上的差距，为学术界和工业界的合作提供了新的可能性。
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+
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+ # Performance
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+
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+ 为了更好地评估SUS-Chat-34B模型的性能，我们在多个基准测试中进行了评估，并开源了评估框架[TLEM](https://huggingface.co/spaces/SUSTech/tlem)，以便于其他研究人员进行复现和比较。
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+
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+ 在TLEM中，我们使用了多个基准测试，包括：MMLU, CMMLU, C-Eval, BBH,
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+ GSM-8K, MATH,
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+ 专注于衡量模型的知识和思维能力，在这些指标中SUS-Chat-34B模型取得了最先进的表现，我们还额外引入了[lm-eval](https://github.com/EleutherAI/lm-evaluation-harness)测试了SUS-Chat和同类模型在winogrande,
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+ hellaswag, arc, truthful-qa的表现, 衡量模型的常识性推理能力和幻觉。
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+
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+ 综合上看，SUS-Chat-34B模型显著领先于同规模的模型，并取得了最先进的综合性能。
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+
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+ | model | mmlu-chat | cmmlu-chat | ceval-chat | gsm8k | BBH | MATH | winogrande | arc | hellaswag | truthfulqa | average |
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+ |:------------------|----------:|-----------:|-----------:|------:|------:|------:|-----------:|------:|----------:|-----------:|--------:|
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+ | GPT-4 | 83 | 71 | 69.9 | 91.4 | 86.7 | 45.8 | 87.5 | 94.5 | 91.4 | nan | 80.1333 |
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+ | SUS-Chat-34B | 77.35 | 78.68 | 82.42 | 80.06 | 67.62 | 28.8 | 81.22 | 81.54 | 83.79 | 57.47 | 71.895 |
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+ | Qwen-72B-Chat | 74.52 | 77.02 | 77.22 | 76.57 | 72.63 | 35.9 | 80.58 | 81.29 | 87.02 | 50.64 | 71.339 |
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+ | DeepSeek-67B-Chat | 69.43 | 48.51 | 59.7 | 74.45 | 69.73 | 29.56 | 76.09 | 82.1 | 86.06 | 56.37 | 65.2 |
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+ | OrionStar-34B | 68.51 | 66.88 | 65.13 | 54.36 | 62.88 | 12.8 | 77.27 | 80.19 | 84.54 | 53.24 | 62.58 |
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+ | Yi-34B-Chat | 66.96 | 55.16 | 77.16 | 63.76 | 61.54 | 10.02 | 76.64 | 70.66 | 82.29 | 54.57 | 61.876 |
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+
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+ <img src="assets/radar.png" id="fig-bench" alt="Figure 2: Benchmark" />
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+
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+ # 用法
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+
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+ SUS-Chat-34B是标准的LLaMA模型，使用方法和开发环境与大多数其它开源模型相同，可以通过以下方式进行多轮对话
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+
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+ ``` python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+
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+ def chat_template(messages):
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+ history = ""
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+ for message in messages:
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+ match message:
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+ case {"role": "human", "content": message}:
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+ history += f"### Human: {message}\n\n### Assistant: "
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+ case {"role": "assistant", "content": message}:
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+ history += message
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+ return history
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+
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+
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+ model_path = "SUSTech/SUS-Chat-34B"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model_path, device_map="auto", torch_dtype="auto"
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+ ).eval()
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+
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+ messages = [{"role": "user", "content": "hi"}]
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+
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+ input_ids = tokenizer.encode(chat_template(messages), return_tensors="pt").to("cuda")
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+ output_ids = model.generate(input_ids.to("cuda"))
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+ response = tokenizer.decode(
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+ output_ids[0][input_ids.shape[1] :], skip_special_tokens=True
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+ )
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+
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+ messages.append({"role": "assistant", "content": response})
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+
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+ # Second round
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+
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+ messages.append({"role": "user", "content": "What is the capital of China?"})
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+
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+ input_ids = tokenizer.encode(chat_template(messages), return_tensors="pt").to("cuda")
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+ output_ids = model.generate(input_ids.to("cuda"))
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+ response = tokenizer.decode(
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+ output_ids[0][input_ids.shape[1] :], skip_special_tokens=True
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+ )
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+
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+ messages.append({"role": "assistant", "content": response})
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+ ```
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+
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+ # 限制
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+
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+ SUS-Chat只进行了监督微调，尚未进行人类偏好学习，因此在一些情况下可能会产生不合理的回复，并放大某些语言模型现有的问题,
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+ 包括幻觉、非确定性和累积误差,
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+ 为了实现更有利于下游任务的性能，我们建议相应地调整生成是配置参数。
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+
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+ # 免责声明
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+
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+ 我们在训练过程中使用数据合规检查算法，尽力确保训练模型的合规性。由于数据复杂且语言模型使用场景多样，我们无法保证模型在所有情况下生成正确和合理的输出。请注意，模型仍然存在产生问题输出的风险。对于因滥用、误导、非法使用和相关错误信息以及相关数据安全问题而导致的任何风险和问题，我们将不承担责任。
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+
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+ # 许可
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+
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+ 该模型完全开发供学术研究和免费商业使用，但需要遵守来自零一万物的[许可](https://github.com/SUSTech-IDEA/SUS-Chat/blob/main/MODEL_LICENSE_AGREEMENT.txt)
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